thesis on wireless communication

How to select a thesis topic in wireless communications

Starting your degree programme is a big step irrespective of which field you are in, and it is easy to think that you are already on track for success because you have started your degree. When pursuing a degree in wider telecoms or wireless communications, your thesis topic can play a crucial role in giving you a taste of your future jobs.

When choosing a thesis topic in wireless telecoms, start with the job market to understand the business problem, pick a topic aligned with your career goals, and do not choose a very mature topic. Think about the value the topic adds to your profile and vice versa, and manage your stakeholders well.

A thesis or dissertation is an essential milestone in pursuing your degree because it provides you with the opportunity to get as close to the industry as possible while still being a student. Once your student life is over, every day you spend between finishing your degree and finding a job adds to the pressure if you don’t have a job lined up. A thesis provides you with a “provisional license” to practice working before your full-time job starts.

The topic needs to be aligned with your career goals

When the time comes for you to decide on a thesis topic, you are expected to have some knowledge of the industry specifics already through your degree programme. It is a good idea to have a view of what areas within your degree programme interest you more from a career perspective.

For example, suppose you find mobile wireless more enjoyable during your degree coursework than fixed wireless. In that case, you need a thesis topic that can help you get the opportunity to work with a mobile operator.

If you are doing a master’s degree, you are expected to be more specific because a master’s degree, i.e. MS or MSc, is a more specialised programme than a bachelor’s degree. Therefore, arguably, your master’s thesis topic is expected to be more specific than a bachelor’s thesis topic.

While you need to respect your passion and career goals, the thesis topic needs to be realistic enough so that you have a good chance of completing it in high quality. For example, if your passion is to become Head of Radio Networks at a mobile network operator (MNO) , your realistic route to your career goals could be a topic that allows you to gain some entry-level experience within RF engineering.

Look at the job market to understand the business problem

The thesis topics that address practical business problems have the best chances of leading to potential job opportunities. The best way to identify business problems is through the job market in your field of study.

In wireless communications , the job opportunities within mobile and fixed wireless operators can help you identify what is in demand in the industry. As your first step, you can go to LinkedIn or any other job portal in your region and search for the ideal engineering jobs within the wireless and telecommunications industries.

Understanding the potential business challenges can take time if you are new to the job market, so do not rush. It may be good to do this in a relaxed way over a period of a few weeks if you have time. Once you have identified some relevant jobs that can potentially be your dream jobs for the future, it’s time to reverse engineer your plan of action.

For example, suppose you’re doing a master’s degree in “Wireless Communications”. In addition, let’s assume that your job search concludes that the jobs that interest you the most within wireless communications are RF engineering jobs. In that case, you can look at the job descriptions to create a general list of responsibilities for RF engineering jobs.

When you look through the job descriptions in the RF engineering area, your list of responsibilities may include things like RF testing, antenna design, high-level system requirements, creating link budgets etc. You can also use the valuable input from your job search to note the business challenges the jobs you came across were addressing.

The business challenges or problems can help you think of the research areas, and the list of responsibilities can help you come up with a list of activities that can be carried out to address the business problems.

Do not choose a topic that is too mature or too futuristic

While you want to go for a research topic that deals with future challenges; you need to be careful not to select a topic that is too futuristic. On the other hand, if you choose a topic where a lot of research has already been done, the chances of you adding something new to the study may not be high.

If you choose an already mature topic, e.g. a research topic within 4G MIMO antennas in 2022, then it is likely that most of what you find may already be well documented. As a result, the research topic might help you learn about 4G MIMO, but it may not be beneficial to a telecom company expected to fund the research.

On the other hand, you also want to avoid choosing a topic that is too futuristic. For example, if you are doing your research in 2022, it will be premature to select the antenna design for 6G networks as a thesis topic when 5G is still in its early days.

A research topic that deals with the short to mid-term future of an already-deployed technology can help you write a thesis that is more relevant to the job market. Using the same RF engineering example as earlier, a sensible thesis topic in 2022 may be one that deals with the evolution of 5G antenna technologies like Massive MIMO as opposed to a research area in 4G or 6G.

Think about the value you can add to the research

A thesis that solves a business problem is a valuable research topic for the company facilitating it. Within telecom companies, especially mobile operators, Research & Development (R&D) departments have allocated budgets to conduct technical or engineering research. These departments have engineering resources, and they have access to the research agencies that carry out the research for them.

As a student with little or no experience, you may find it a bit intimidating if a company already has a pool of resources to carry out the research for them. You may even have questions like, “Do they really need me?” or “Do I have enough knowledge to do the research?”. Of course, it is natural to have questions like these, but the good news is that companies need students.

The engineering teams within a telecom company are generally pretty busy, and the internal resources do not always have the spare capacity to focus on a research topic without some help. If the company looks for help from an external agency, it will always cost considerably more than hiring a student for a few months. As a student interested in doing research, that is your opportunity window.

Most research starts with desktop research or documentation review, where having a student with relevant background is helpful. The students work with internal resources who can provide the necessary guidance to ensure that the research is reliable and of high quality.

A passionate graduate student can use their intellectual prowess and learning skills to conduct a thorough study of the available research material, including whitepapers, online journals and manuals etc., to build the narrative in close collaboration with the experienced resources within the company.

Think about the value the topic can add to your profile

While it may be natural for you to think of the telecom company that acquires your research skills as the superior authority, it is vital that you also think about the value the research topic adds to your profile.

For example, if you are a student of wireless communications, a thesis topic on Plain Old Telephone System (POTS) may not be a great match, even if it is the quickest thesis opportunity you can find. Finding a relevant topic that matches your academic background gets the best out of you.

If you cannot find a thesis topic that matches your academic background perfectly, it is OK to broaden the scope of your search. Using the above example, a research topic on POTS may not be an excellent choice for a wireless communications student. However, since it is still part of the broader telecom industry, it can still be relevant.

A thesis is also a great opportunity for you to work with many like-minded people who operate in the same industry as you and who can provide you with the guidance and support you need in progressing with your career.

Your thesis report is not the only measure of success

Your thesis report is an important research paper that you need to complete your degree. However, the actual value of the research is based on your findings rather than how well-written your thesis report is. The completion of your degree relies on the thesis report, but your career after the degree benefits more from your research findings.

You need to follow your university guidelines to make sure you meet the criteria for the research, but it is important to focus more on the content than the cosmetics of the report. The real measure of success for a good thesis, like any research, is the value its outcome adds to the existing knowledge base.

For you professionally, a successful thesis needs to ensure that the outcome will be reusable while adding value to your profile (CV).

For example, suppose your thesis is on advanced antenna systems for 5G networks to determine whether a specific antenna configuration improves network throughput. In that case, the desired outcome for you may be the possibility to test different 5G antenna configurations within a lab or field environment on an actual 5G network.

If your research achieves the desired results, it’s a successful thesis, even if the report is not written in the best possible way. On the other hand, if your research thesis did not achieve the desired outcome, then even a very well-written dissertation may not be of great value to you at a professional level.

There is a clear difference between a theoretical thesis based on online research and a thesis that relies on applying theory within a lab or field environment.

Manage your stakeholders to your advantage

The final tip I have for you is to carefully manage your internal and external stakeholders. Internal stakeholders are people from your university, e.g. your advisor (teacher), while the external stakeholders are people from the company where you are conducting your research. If you are doing your thesis as a group of students, then the students in your group are also your internal stakeholders.

You want to avoid a situation where the decision-making for your thesis becomes more challenging due to any potential disagreements between internal and external stakeholders. For example, if the company wants you to spend a few days on field testing for an actual 5G antenna system, but your internal advisor is asking you to focus more on lab-based 5G antenna simulations for your report, you may find it hard to disagree with either of your stakeholders.

The best way to avoid any such conflicts is to plan the thesis well because most of the activities you carry out as part of your research will be based on the deadline for your thesis report. The lead times for the company will almost always be longer, so you need to plan as many useful professional activities within the available timeframe as possible.

If you have a choice, try to find an “easy-going” advisor from the university because you will have less flexibility at the company where things follow the norms of the business rather than what your university expects.

Also, if you are doing your thesis as a group rather than as an individual student, be mindful of any potential competition. There is value in doing a thesis project as a group to spread the workload, but there is a trade-off between that value and internal competition. If you cannot find a like-minded group of students, it is better to do it independently.

About The Author

Adnan Ghayas

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Design and Feasibility Verification of 6G Wireless Communication Systems with State of the Art Technologies

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• Published: 28 December 2021
• Volume 29 , pages 93–117, ( 2022 )

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• Ravilla Dilli   ORCID: orcid.org/0000-0003-0450-8584 1  

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Frequencies above 100 GHz are the promising frequency bands for 6G wireless communication systems because of the abundant unexplored and unused spectrum. The increasing global demand for ultra-high spectral efficiencies, data rates, speeds and bandwidths in next-generation wireless networks motivates the exploration of peak capabilities of massive MIMO (Multi–Input–Multi–Output) wireless access technology at THz bands (0.1–10 THz). The smaller wavelengths (order of microns) of these frequencies give an advantage of making high gain antennas with smaller physical dimensions and allows massive spatial multiplexing. This paper presents the design of ultra-massive MIMO (ultra-mMIMO) hybrid beamforming system for multi users and its feasibility to function at THz frequency bands. The functionality of the proposed system is verified at higher order modulation schemes to achieve higher spectral efficiencies using performance metrics that includes error vector magnitude, symbol constellations, and antenna array radiation beams. The performance results suggest to use a particular mMIMO antenna configuration based on number of independent data streams per user and strongly recommended to use higher number of data streams per user in order to achieve higher throughputs that satisfy the needs of 6G wireless systems. Also the performance of the proposed system at 0.14 THz is compared with mmWave systems that operate at 28 GHz and 73 GHz bands to justify the feasibility of the proposed work.

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1 Introduction

As the 5G wireless technology will be inadequate in the near future due to explosive growth of D2D networks like IoT, IIoT, and IoS. Next generation wireless communication systems, 6G is aimed at supporting communication needs of 2030s. Therefore, 6G wireless technology is essential which uses terahertz (0.1–10 THz) frequencies of EM spectrum and supports 10–100 GHz bandwidths, data rates of Terabits/sec 6G wireless system architecture is an integrated system of different radio access technologies with ultra-massive scales and dynamic network resources to achieve diverse performance improvements. The radio network architecture of 6G cellular systems should make use of cognitive radio, carrier aggregation, THz bands, ultra-mMIMO, and femto cell-based heterogeneous networks to provide maximum capacity [ 1 , 2 , 3 ]. The main aim of this article is to propose and design a hybrid beamforming system for 6G wireless networks that consist of ultra-mMIMO antenna array configurations of size varies from 64 to 256 at BS and 4–16 at MS of multiple users that operates at 0.14THz frequency band. The performance measurements at 0.14 THz are conducted and feasibility of the proposed system is verified for higher order QAM modulation schemes, also compared with mmWave hybrid beamforming systems that operate at 28–73 GHz bands.

1.1 Applications of mmWave and THz Frequency Bands

6G use applications include autonomous driving, industrial automation, machine-to-machine communications that have hard thresholds in terms of throughput, latency, and reliability. There are challenges in 6G system architecture design, network and resource management.

The mmWave and THz applications mentioned in Fig.  1 and Table 1 demand data rates of the order 100 Gbps and wider bandwidths (in terms of GHz) which can be possible with transmission schemes whose minimum spectral efficiencies are 14bits/sec/Hz. Unavailability of such digital modulation schemes and transceiver components to achieve higher data rates in the current scenarios motivates to explore the frequency spectrum above 100 GHz. “Information showers(IS)” provide extreme data rates (> 1Tbps) for ultra-short range wireless networks at THz frequencies. IS makes massive offloading of heavy traffic (up to 95%) in long-range networks and improves UE’s energy efficiency [ 5 ]. In wireless cognition applications, massive computations are conducted on a high capacity remote device like centralized servers. Low power drones (example of resource constrained robots) having limited on-board computing facilities takes advantage of cloud robotics and offload the computations to a cloud server [ 6 ]. In drone based cellular networks (3D), BSs and UEs uses drones to ensure full coverage with less number of BSs in the given space. 3D cellular networks achieve up to 46% reduction in average delay at UEs that includes backhaul delay, transmission, computation delays, also improves spectral efficiency [ 7 ]. GPS based navigation systems are vulnerable to jamming via RF interference, as an alternate THz signals can be used to have high precision range measurements [ 8 ]. Sensing applications mentioned in Table 1 exploit the absorption characteristics of channel materials, frequency-selective channel fading, ultra-mMIMO, and wider bandwidths at THz frequencies. Electronic beam steering techniques and ability of “wireless reality sensing” allows to generate real-time high resolution 3D maps of any given location with less latencies (< 10 nsec). At mmWave and THz frequencies, the effect of ambient light and weather have lesser impact on radar channel than LIDAR. Dual-frequency radars at THz bands give high resolution (similar to high-definition videos) under heavy rain or foggy, cloudy weather conditions whereas LIDAR have very poor performance under such conditions. “Dark vision” is the ability to reconstruct 3D maps of unknown environments at mmWave and THz bands using the combined techniques of sensing, imaging, and position location. Imaging methods at THz bands have less computational complexity in reconstruction of images and less hardware complexity than optical imaging methods even under NLOS channel conditions. THz time-domain high-resolution images can be generated by radar systems for objects irrespective of their shape, size, position and orientation where edges (edge lengths less than 400 µm) are clearly detected [ 9 ]. By exploiting mMIMO beam steering, arrival time/angle of uplink pilot signals one can generate the mmWave image of surrounding environment (both LOS and NLOS objects). Presence of ultra-mMIMO antenna arrays and large available bandwidths at mmWave and THz bands made possible to locate both LOS and NLOS users with centimetre precision [ 10 ].

EM spectrum and possible Applications [ 4 ]

Optical wireless communication technologies such as free space optical communication, light fidelity, visible light communication, and optical camera communication can provide effective solutions for deployment of 6G systems and IoT [ 11 ]. Combination of free-space optical and mmWave systems provide significantly high data rates in wireless transmissions in spite of rapid variations in channel and environmental conditions. The selection of active link is determined by index modulation that does not need CSI or any feedback at Tx and it provides higher spectral efficiency [ 12 ]. The combination of optical and digital system architectures at THz bands provide data rates of Terabits/s, nearly zero latency and bandwidths greater than 50 GHz. The front-end transceivers are designed such that they support higher-order modulation schemes to provide ultra-high spectral efficiency [ 13 ].

This paper is organized as follows: Sect.  1 provides insight into mmWave and THz applications. Section  2 explores design challenges, key enabling technologies, role of AI, security aspects in 6G wireless networks. Section  3 describes the wave propagation, channel modelling and measurements at THz bands. Section  4 explores the possibility of using ultra-massive MIMO hybrid beamforming techniques for improved front-end performance at frequencies above 100 GHz. Section  5 discusses the results of ultra-massive MIMO hybrid beamforming system at 140 GHz and compared with the performance at 28 GHz, and 73 GHz mmWave frequencies [ 14 , 136 ]. Section  6 gives overall observations, conclusions and possible future directions of research that can be carried out towards THz frequency bands.

2 6G Wireless Communication Systems

2.1 design challenges and key enabling technologies.

The connectivity goals of 6G wireless systems include: usage of THz frequencies to have abundant bandwidths, pervasive AI, cell-free mMIMO, backscatter communication to save energies, massive network automation etc., Beyond 6G, technologies like internet of nano and bionano things, quantum communications are expected to have impact on wireless communication systems [ 15 ]. Free-space optical network, wireless optical technology, AI, THz, blockchain, quantum communications, 3D networking, driverless vehicles, integrated sensing and communication, cell-free communications, integration of wireless information and energy transfer, backscatter communication, integrated access-backhaul networks, dynamic network slicing, intelligent reflecting surface, holographic beamforming, big data analytics, and proactive caching are the key enabling technologies to support 6G wireless systems development with guaranteed QoS [ 16 ]. In spite of technical challenges, it is possible to implement 6G wireless networks above 100 GHz for the applications shown in Table 1 . Using special theory of relativity, computational complexity of signal processing in adaptive antenna arrays can be minimized and improve the performance of digital phased array antennas. The new path loss, partition loss and channel models (that considers correlation over space and minute changes) are defined for frequencies above 100 GHz.

“Unmanned aerial vehicles (UAV)” communication is one of the important components of 6G wireless technologies where flexible deployment, high degree of design freedom with controlled mobility, strong LOS paths are expected [ 17 ]. UAV-mounted flying BS allow data transmissions to a number of radio devices spread over the area. Optimized trajectories for high throughput and efficient discovery of channel parameters can be designed in UAV communication systems [ 18 ]. UAV communication provides high QoS in deep rural, difficult-to-serve areas, disaster-stricken areas using relay signals. 6G wireless networks are open and intelligent to support dynamic services in IoT which demands for convergence of computing, caching and communicating (3C). 6G wireless technologies support 3C-based spectrum management, wireless distributed computing, delay-aware transmission and network selection [ 19 ]. Deployment of smart health-care systems using 5G wireless technologies have challenges in terms of device density, latency, bandwidth, security, and energy efficiencies [ 20 ]. It is important to focus on study of channel, devices, and space-based system at mmWave and THz frequencies [ 21 , 22 ]. 6G wireless technologies are the key for IoE applications that integrates applications ranging from virtual reality to autonomous systems [ 23 ]. 6G wireless technologies are expected to have high energy efficiency and technical standard of new spectrum to address the challenges including network designs, testbed developments, physical payer transmissions, and security [ 24 ]. High data rates can be achieved at THz bands such as 0.275–3 THz, however there are challenges in designing adaptive transceivers (both RF and digital domain), signal processing algorithms, channel characterization, privacy, and security [ 25 ].

6G wireless network features include high-precision 3D localization, ultra-low latency, high reliability, and high bandwidths leads to native applications like distributed, ubiquitous AI, IoT. In 6G wireless networks, the key enabling technologies such as ultra-mMIMO, THz, a tactile internet should address security aspects in 6G [ 26 ]. The design and development challenges in high-speed T–R modules, ultra-wideband antennas, channel modelling, higher order modulation schemes at THz bands are the new frontiers of 6G wireless systems [ 27 ]. Investigation of nonlinear optical effects and nanoscale phenomena at THz frequencies are essential as THz bands play an important role in 6G wireless networks [ 28 ].

2.2 Role of AI in 6G Wireless Networks

AI and machine learning techniques are most important features in making intelligent 6G networking systems [ 29 ]. AI plays a crucial role in 6G network designs and protocols to support mobile internet as well as ubiquitous AI services for the users [ 30 ]. Next generation driving use cases (vehicular communication: vehicle-to-everything applications) are AI based which is an emerging field in 6G vehicular networks that demands high reliability, ultra-low latency, high data rates, and high security [ 31 , 32 ]. To address these use cases, mmWave mMIMO beamforming techniques are essential with beam alignment and beam sweeping capabilities. Also, optimal beam sweeping schemes have to be introduced at Tx and Rx to reduce the latency in achieving beam alignment [ 33 ]. AI-powered applications demand 6G wireless technologies and motivates to in-depth research possibilities in terms of coverage, spectrum, energy consumption, air interface, and delay [ 34 ]. Applications of 6G wireless networks can get potential benefits with the synergies of quantum computing, machine learning and deep learning techniques [ 35 ].

AI based architecture for 6G includes flexible radio access network-slicing, mobile edge caching & content delivery, and automated radio access technology selection [ 36 ]. Air-interface designs (integration of communication with radio sensing) in 6G wireless technologies are optimized by AI techniques to meet the latency, reliability and synchronization requirements [ 37 ]. AI-enabled 5G and 6G cellular networks deployment provides significant improvements in performance and robustness at lower complexity [ 38 ]. AI-based adaptive scheme for 6G IoT networks provides security where IoT devices are connected to mobile networks using different THz and mmWave frequency bands. Extended Kalman filtering in 6G networks can estimate future harvesting power and avoid energy exhaustion [ 39 ]. Deep learning-based (using a deep neural network structure) mmWave beam selection in 5G NR by exploiting sub-6 GHz channel information minimizes beam sweeping overhead by 79.3% [ 40 ]. Heterogeneous multi-layer mobile edge computing in which MSs generate raw data of computing tasks in radio access networks to cloud centre that involves collaborative data processing at various network layers. It is essential to have ultra-low latency service in 6G networks which is possible by reinforcement learning in pricing scheme design, network congestion control, cognitive radio access, and task offloading for volatile wireless environments [ 41 ]. 6G mobile network architecture is expected to operate at THz frequencies with high QoS support and energy efficiency. The architectural challenges of 6G networks include 3D coverage, efficient protocol stack, pervasive AI [ 42 ]. AI (deep learning methods) techniques optimize the performance of space-air-ground integrated networks by improving traffic balancing [ 43 ]. Hybrid beamforming designs for mMIMO with minimum number of RF chains and phase shifters based analog beamforming designs are challenging with imperfect CSI [ 44 ]. “Multitask deep learning” techniques are used in hybrid beamforming for MU-mMIMO OFDM systems [ 45 ]. Deep learning based beamforming designs maximizes spectral efficiency with imperfect CSI and less hardware.

2.3 Security Aspects in 5G and 6G Wireless Systems

It is challenging to implement security in 5G and 6G wireless networks as they are deployed in decentralized, heterogeneous, and massive ubiquitous devices. Blockchain technology can provide security and privacy for 5G and 6G networks like edge computing, cloud computing and device-to-device communications [ 46 , 47 ]. 6G applications scenarios demand for high privacy, secrecy, and security [ 48 ]. Physical layer level security techniques are essential at THz frequencies to address threats like eavesdropping which is possible by placing an object and intercept the LOS signal to get scatter radiation in the direction of eavesdropper. However, characterization of backscatter in channel addresses the problem of eavesdrop and also detects eavesdropper [ 49 ]. Security can be provided in mmWave mMIMO wireless systems using channel sparsity where the data is precoded onto dominant angle components while artificial noise in broadcasted over non-dominant angles which interfere with eavesdropper’s signals. The entropy of channel sparsity (is unknown to eavesdropper) defines secrecy rate and optimal levels of sparsity provides maximum secrete rate [ 50 ].

3 Wave Propagation at THz Frequencies

3.1 wave propagation scenarios (reflections, diffraction, scattering).

The radio signal propagation undergoes through three main propagation mechanisms called reflection, diffraction and scattering in multipath channel as shown in the Fig.  2 [ 51 ]. At THz carrier frequencies, signal transmissions are highly directive (less angular divergence) and effects of diffraction is minimum. Adaptation of mMIMO, hybrid beamforming or beam steering techniques overcomes the shadowing effects at mmWave frequencies [ 52 ].

Wave propagation in a multipath fading channel

Scattering is an important propagation mechanism to consider in channel modelling at THz frequencies for 6G wireless systems. The surface scattering at THz frequencies exhibit both diffuse and strong specular reflections. “Directive scattering” and “radar cross section” scattering models support development of ray tracing tool design, imaging, and localization for 6G wireless systems [ 53 ]. Characterization of surface scatterers (for both diffused and specular components) at 60 GHz leads to small-scale fading due to rough surface scatterers. The diffused scattering depends on distance from the surface, angle of incidence, and material roughness [ 54 ]. The multipath delay spread and path losses of a radio channel are characterized at frequency range 126–156 GHz [ 55 ].

Wavelengths of THz frequencies approach the dimensions of dust, snow particles or rain and effects of Mie scattering becomes more significant contribution to link budget. The rain effects do not contribute to additional attenuation losses at THz frequencies (0.1–0.5 THz) [ 56 ]. Atmospheric gases like hydrogen, oxygen, and carbon-di-oxide etc., causes significant attenuation at THz bands (183 GHz, 325 GHz, 450 GHz, 550 GHz, 760 GHz) and these frequency bands can be used for secure ultra-short range communications [ 57 , 58 ].

3.2 Channel Modelling and Measurements

Channel properties define the performance limits in 6G wireless communication system. So, it is very important to explore different 6G channel models, measurement techniques and develop statistical channel impulse response models both in space and time at THz frequencies [ 59 , 60 , 61 ]. 3GPP defines human blockage, spatial consistency, and outdoor-to-indoor penetration losses as main components of channel modelling for mmWave mMIMO hybrid beamforming system designs. A geometry-based, spatially consistent channels are realized by generating time-variant and spatially correlated channel coefficients. Human blockage shadowing losses are modelled at 73 GHz using four-state Markov model [ 62 , 63 , 64 , 65 , 66 ]. Radio propagation models or channel models (both large-scale, small-scale fading, also MIMO channels) for mmWave 5G wireless systems are defined with hybrid beamforming and coordinated multi-point transmissions at 24 GHz to 86 GHz frequencies. The performance metrics (coverage, spectral efficiency, hardware requirements) are highly dependent on channel models [ 67 ]. mmWave technology provides adaptive tuning in military communication networks as per atmospheric variations and high node mobility without compromise for high data rates [ 68 ]. Various channel models and propagation parameters of 5G wireless communication systems such as building penetration losses, large-scale path loss, LOS probabilities helps to understand the propagation of mmWave frequencies over 0.5–100 GHz [ 69 ].

In the presence of CSIT, antenna array gains and spatial multiplexing gains are fully utilized in mMIMO systems. But, conventional CSIT techniques do not suit FDD mMIMO systems due to their feedback and training overhead. Three pilot training strategies are proposed for acquiring CSI in MU mMIMO mmWave systems using “generalized block OMP algorithm” that performs better than conventional OMP algorithm [ 70 ]. Joint optimization technique of communication and computation powers is formulated for MU mMIMO systems with partially-connected RF structures that saves powers up to 76.59% [ 71 ]. “Generalized-block compressed sampling matching pursuit algorithm” offers better performance in acquiring CSI and minimizes errors compared to “generalized block OMP algorithm” [ 72 ]. Using a distributed compressive sensing CSIT, feedback and training overhead can be minimized in MU mMIMO hybrid beamforming systems [ 73 ].

Requirement of high data rates and bandwidths for smart rail mobility motivates to explore high gain mMIMO, advanced handover designs, and dynamic beamforming techniques at mmWave and THz bands [ 74 ]. The communication between passengers and rails as well as intra-wagon scenarios are characterized using ultra-wideband channel sounding and ray tracing at 60–300 GHz frequency bands with 8 GHz of bandwidth [ 75 ]. The measured propagation losses of a communication link at 350 GHz frequency estimates the data rates of 1Gbps at 8.5 m and 100Gbps for 1 m distances [ 76 ]. Channel models and propagation measurements are presented at D-band (110–170 GHz) frequencies. Channel sounder system is designed for 140 GHz and signal attenuation measurements are investigated for indoor models [ 77 , 78 ]. The large-scale path loss and multipath time dispersion measurements for ultra-dense indoor wireless networks at mmWave bands show that multipath RMS delay spread can be minimized using more directive antennas [ 79 ]. Certain mmWave and THz frequency bands exhibit less path loss (< 10 dB/Km) in addition to free-space path loss values. Therefore, these frequencies can be used for 6G cellular and mobile applications (long-range communications) [ 80 ].

THz frequencies exhibit a very high signal attenuation or path loss, however, we can overcome this using ultra-mMIMO antenna arrays which compensate increased path loss and provides highly reliable links. Neighbour discovery is challenging in mMIMO systems at THz bands and this process can be made time-efficient by using the complete antenna radiation pattern instead of only main-lobe [ 77 , 81 ].

3.3 Channel Measurements at 73 GHz

Indoor wave propagation mechanisms (scattering, reflection, path-loss) and channel properties are studied at mmWave and Terahertz (28 GHz, 73 GHz and 140 GHz) frequencies. At these frequencies, reflection coefficient is linearly increasing with increase in angle of incidence and the scattered power is 20 dB less than the reflected power. Also, for a given angle of incidence, path loss is inversely proportional and partition loss (material dependent) is proportional to frequencies [ 82 ]. mmWave multipath signal propagation characteristics for indoor communication were measured in terms of power received, path loss and delay spread at 28 GHz, 39 GHz, 60 GHz, and 73 GHz frequencies. The received power and delay spread are inverse in relation to T–R separation, whereas path loss is proportional to T–R distance. The amount of path loss due to multi-floors is very high at 28–39 GHz compared to 60–73 GHz [ 83 ]. Coordinated Multipoint and BS diversity measurements of a large-scale mmWave 5G wireless system at 73 GHz shows the improvements in interference suppression where 43% of users overcome interference [ 84 ]. Penetration losses, outdoor foliage induced scattering, and ground reflection measurements are conducted at 73 GHz, 81 GHz with both cross-polarized and co-polarized antenna configurations to provide consistency and reliability in measurements at any given frequency [ 85 , 86 , 87 , 88 ].

Beam steering capabilities of directional antenna arrays at mmWave bands can overcome the path loss of a multipath channel in a dense urban cellular environment [ 89 ]. Large-scale path loss models based on ultra-wideband mmWave propagation measurements at 28–73 GHz offer simplicity in path loss calculations and predictions with high accuracy. 3GPP floating-intercept path loss model needs a simple modification for converting in to the close-in free space reference distance models [ 90 ]. Indoor and outdoor channel measurements at 100 GHz, 200 GHz, 300 GHz, and 400 GHz are carried out for a link data rate of 1Gbps. The multi-path channel measurements proved the feasibility of using THz radio links for data transmissions in a highly fading channel environments [ 91 ]. The radiation at THz (0.1–10 THz) bands penetrates through materials (indoor office) with minimum penetration losses and loss increases for higher end of THz band [ 92 ].

4 Ultra-mMIMO and Hybrid Beamforming Techniques for 6G

mMIMO and ultra-mMIMO are the key enabling technologies in 5G and 6G networks respectively with efficient beam steering capabilities. The important open research challenges in mMIMO system design are channel estimation, pilot contamination, user scheduling, precoding, signal detection, and energy efficiency [ 93 ]. The mMIMO and ultra-mMIMO beamforming systems offers huge bandwidths with very high data rates in 5G and 6G cellular mobile systems. They provide maximum antenna array gains by overcoming the high path loss, atmospheric absorption, and rapid channel variations. These designs demand precise alignment of BS and UE beams that increases latency in link establishment, implications of control layer procedures like beam tracking, and handover [ 94 ].

6G wireless technologies use ultra-mMIMO antenna arrays and THz frequencies to support AI-infused applications, autonomous driving, IIoT, wireless brain–computer interaction, wireless backhaul, augmented/virtual reality, and holographic applications [ 95 ]. Design and development of RF antenna systems for beyond 5G and 6G are very important as the operating frequencies are very high. It is important to focus on breakthrough performance of antenna system with new materials and metamaterials, evolution of propagation channels, and updates in antenna measurement technology [ 96 ]. ultra-mMIMO communications can overcome the higher propagation losses at THz bands and increase the communication range. Graphene-based plasmonics allow us to design large antenna array structures within small area (e.g., 144 antenna elements per 1cm 2 at 60 GHz, 1024 antenna elements per 1 mm 2 at 1 THz) and enables ultra-mMIMO communication in 6G wireless systems [ 97 ]. “quantum-inspired social emotional optimization” algorithm achieves optimal power control and QoS in mMIMO UL networks for maximizing spectral efficiency [ 98 ]. Accuracy of beam selection, beam pair update delay effects the QoS performance. Best measurement techniques for mobility and beamforming management needs to be followed to have more accuracy in 3GPP NR mmWave systems [ 99 ]. In a MU mMIMO systems, low complex solutions are proposed to reduce Tx power based on individual SINR constraints for users less than and greater than the number of RF chains [ 100 ]. Low complex inter-user interference reduction algorithms are used in MU mMIMO UL systems to achieve performance close to fully digital beamforming system [ 101 ].

To minimize the hardware cost of MU mMIMO systems, “hybrid beamforming with selection” considers instantaneous channel variations using switch bank architecture [ 102 ]. Joint user scheduling is proposed for MU mMIMO OFDM systems where users with identical radiation beams are multiplexed using OFDM to improve the system performance [ 103 ]. Accuracy of channel estimation in MU mMIMO systems is improved using its continuous representation instead of grid-based as a result there is an improvement in spectral efficiency [ 104 ]. A novel hybrid beamforming design is proposed to improve the total sum rate based on “weighed sum mean square error minimization” and “compressed sensing” under perfect CSIT [ 105 ]. “Manifold optimization”, “eigenvalue decomposition”, and OMP hybrid beamforming algorithm is proposed for mmWave systems to minimize the computational complexity, mean square error and increase transmission reliability as well as spectral efficiency [ 106 , 107 ]. Relay assisted MU mMIMO hybrid beamforming mmWave system can overcome the path loss and improves system performance [ 108 ]. Splitting of hybrid precoding and combining between Tx and Rx along with OMP algorithm increases the total sum data rates in a MU mMIMO systems [ 109 ]. In MU mMIMO hybrid beamforming systems, under imperfect CSI, radiated power of UEs is inversely proportional to square-root of the number of BS antennas whereas with perfect CSI, radiated power is just inversely proportional to number of BS antennas [ 110 ]. If the number of BS antennas are minimum, MRC performs better than ZF and as number of BS antennas are increasing, uplink data sum rates for MRC and ZF becomes equal. Total data sum-rate is maximized by minimizing weighted sum MSE (based on block diagonalization in digital beamforming) in DL MU-mMIMO hybrid beamforming systems with perfect CSI. Lower capacity bounds for MRC, ZF and MMSE detection are derived. Generally, ZF and MMSE performs better than MRC Rx due to their ability to cancel intra-cell interference. In multi-cell environment, at lower power levels, cross-talk introduced by inferior MRC falls below the noise level and this Rx becomes a viable option. For example, in a small-scale Rayleigh fading channel, 50 MSs can be served with 100 an antenna array of 100 at BS using same time–frequency resources. Therefore, each MS has 1bits/channel/MS and it leads to a total system throughput of 50bits/channel/MS.

mmWave mMIMO DL hybrid beamforming with geographically distributed cluster antennas over the coverage area (remote antenna arrays) decreases the average distance between UEs and arrays, also provides more spatial diversity. This design has performance same as fully digital beamforming in terms of spectral efficiency per user when the number of RF chains are not less than twice the number of UEs [ 111 ]. mmWave mMIMO lens antenna array reduces the number of RF chains considerably in the presence of CSI. “Approximate message passing learned prior-aided Gaussian mixture” beamspace channel estimation algorithm can give more accuracy [ 112 ].

4.1 MU-mMIMO Hybrid Beamforming

In a multi-user communication, multiple users share the same channel, therefore communication needs to be coordinated in T–F domains. In MIMO systems, the Tx power is divided and provide both diversity gain as well as antenna array gain. MIMO beamforming improves spectral efficiency and coverage is high in a particular direction. Hybrid beamforming can be categorized depends on average or instantaneous CSI where instantaneous CSI provides better SNIR and average CSI gives lower overhead in CSI acquisition. Therefore, algorithm selection to get best trade-off essentially depends on channel characteristics [ 113 ]. CSI availability at Rx (CSIR) is collected using pilot signals and CSI at Tx (CSIT) is collected using feedback channels from Rx to Tx. If we have CSIT, the Tx signal can be precoded to provide diversity gain at the Rx, on the other hand if we have CSIR then it is possible to provide diversity using “space–time coding”. MU-MIMO provides all-to-all communication (between K-access points and K-UEs where each UE has multiple independent data streams) and facilitates higher spectral efficiency (over mMIMO) using an interference cancellation beamforming algorithm. Shannon’s limit for channel capacity is defined with K > 3 under Rayleigh fading channel conditions and defined the upper limit for MIMO antenna array size to have an optimum throughput and overhead [ 114 ]. Coordinated multipoint techniques in a multi-cell MU-mMIMO hybrid beamforming system can mitigate intra-cell and inter-cell interference, also achieves high spectral efficiency by maximizing signal-to-leakage-plus-noise ratio [ 115 ].

Data processing steps in a MU mMIMO systems are shown in Fig.  3 where the source encoded user’s data symbols are encoded (channel encoding) using convolutional codes at the Tx. The channel encoded bit stream is mapped to QAM symbols and the resultant QAM data of every user is split into multiple independent data streams. Precoding weights (computed using HBPS algorithm) are assigned for each subcarrier of independent data stream at Tx. The same precoding weights are used to derive the corresponding combining weights at Rx. HBPS algorithm provides all digital beamforming weights and recognize peaks to form corresponding analog beamforming weights. The digital precoded signal is modulated using OFDM with pilot mapping followed by analog beamforming process at Tx antennas. The transmitted signal travels through a scatter rich MIMO channel and this signal is demodulated, and decoded at Rx. Channel sounding and estimations processes are performed using JSDM algorithm that permits large scale MIMO antenna arrays at BS with less CSI feedback information from UEs of a mMIMO DL channel.

Data processing steps in a MU-mMIMO hybrid beamforming system

4.2 Wideband mMIMO-OFDM Systems

OFDM technique eliminates ISI by distributing high-rate user’s data across many number of narrowband sub-channels (those are closely spaced) to provide reliable broadband communication. When the bandwidth is sufficiently large, the channel between any Tx antenna and Rx antenna will appear dispersive. Because of this, we can combine MIMO with OFDM for frequency selective channels as shown in Fig.  4 . We can perform this by connecting OFDM Tx to each transmitting antenna and OFDM Rx to each receiving antenna, and process the signals across different antennas.

mMIMO-OFDM system

Using MIMO-OFDM, we can convert wideband mMIMO channel into many parallel narrow band MIMO channels. All practical mMIMO systems operate in a wideband region, therefore, mMIMO can be combined with OFDM for frequency selective channels. The combination of MU mMIMO-OFDM gives extremely high spectral efficiencies even in the absence of CSIT. In the presences of CSIT, channel capacity of MU mMIMO-OFDM systems approach the theoretical limits and these systems are essential for beyond 5G wireless communications.

4.3 Channel Modelling in MU-ultra-mMIMO Systems

The mMIMO channel can be characterized as

The channel matrix, \({\mathbf{H}} = \left[ {\begin{array}{*{20}l} {h_{11} } \hfill & {h_{12} } \hfill & \ldots \hfill & {h_{{1{\text{Mt}}}} } \hfill \\ {h_{21} } \hfill & {h_{22} } \hfill & \ldots \hfill & {h_{{2{\text{Mt}}}} } \hfill \\ \ldots \hfill & \ldots \hfill & \ldots \hfill & {h_{{3{\text{Mt}}}} } \hfill \\ {h_{{{\text{Mr}}1}} } \hfill & {h_{{{\text{Mr}}2}} } \hfill & \ldots \hfill & {h_{{{\text{MrMt}}}} } \hfill \\ \end{array} } \right]\) .where h ij is a gaussian complex random variable which models fading gain between jth Rx antenna and ith Tx antenna.

For any given subcarrier ‘k’, the vector observation is given as,

where y  → Rx column vector of size M r  × 1, H  → channel matrix of size M r  × M t , X  → Tx column vector of size M t  × 1, n  → AWGN noise column vector of size M r  × 1, z k  → observation vector of size M r  × 1.

High accurate and low complex channel estimation scheme called “Step-length optimization-based joint iterative scheme” is developed for MU mMIMO system that uses less number of training sequences even for large number of users [ 116 ]. Hybrid channel estimation is proposed where the channel matrix is decomposed into an angle matrix and gain matrix. This technique improves channel estimation accuracy with minimum training overhead in a MU mMIMO hybrid beamforming systems [ 117 ]. Lens antennas array based MIMO needs less number of RF chains as it exploits the property of angle-dependant energy focus. “Path delay compensation” techniques that exploits multi-path sparsity converts MU MIMO channels that are frequency selective into parallel narrow-band flat fading channels [ 118 ]. Optimizing hybrid beamforming design of relay system with full-connected and mixed structures maximizes the sum rate in a mMIMO systems [ 119 ].

The changes in angles of arrival/departure (AoAs/AoDs) are less compared to their path gains in a MU mMIMO systems. MSE minimization of received signal using the solution of digital beamforming gives maximum sum rates in a mmWave hybrid beamforming system. The sum rate increases with number of paths (accuracy of AoA/AoD) and number of RF chains [ 120 ]. The estimation of AoA/AoD helps in pilot beamforming and maximizes it power to achieve accurate path gains [ 121 ].

4.4 Channel Estimation in MU-ultra-mMIMO Systems

Estimation of more channel coefficients in a MU-mMIMO systems and high propagation loss in mmWave transmission give a challenge in channel estimation for DL. The BS antenna array uses imperfect CSI that is derived using transmitted pilots signals to extract individual data streams. The radiated power of MSs is inversely proportional to square-root of number of BS antennas with no reduction in performance. If perfect CSI is available, transmitted power at MSs can be made inversely proportional to number of BS antennas. For complete utilization of spatial multiplexing gains (or antenna array gains) of mMIMO, CSI must be obtained at the Tx side.

Joint design of digital and analog stages overcomes loss of information at each stage and this gives optimal solution for MU-mMIMO hybrid beamforming system whose sum rate approaches channel capacity [ 122 ]. The users are grouped into clusters based on feedback and CQI of beams in a mmWave MU mMIMO DL NOMA system which offers maximum sum rates [ 123 ]. Angular representation of channel using non-zero sparse matrix to form clusters in a MU-mMIMO systems better than the system without clusters [ 124 ]. “compressive sensing” techniques reduce the channel measurement complexity and configure hybrid precoders & combiners in a mmWave MU MIMO system with frequency selective channels [ 125 ]. Usage of “penalty dual decomposition” method in hybrid precoding approaches the fully digital precoding performance even in the presence of less number of RF chains at Tx and Rx [ 126 ].

Diagonalization of channel matrix provides unconstrained optimal precoding weights by taking the first N T RF dominating modes under known CSI conditions. Most of the time, CSI is at Rx (CSIR) is acquired using pilot signals which carry channel estimation information. Sometimes, using feedback channels from Rx to Tx we can also collect CSI at Tx (CSIT). If we have CSIR, we can provide diversity gain using “space–time coding”, on the other hand, if CSIT is available, we can precode the transmitting signal to provide diversity gain at the Rx. Under the availability of CSI at BS, MU precoding is performed to transmit signals to all users using same T–F resources and UEs can recover signals with minimum complexity [ 127 ].

Design of two stage beamformers for FDD MU mMIMO DL hybrid beamforming systems optimizes hybrid precoding to increase sum rates and conditional average signal-to-leakage-plus-noise ratio [ 128 ]. Optimal hybrid precoding and combining algorithms based on the principle of basis pursuit demands only low cost RF hardware in mmWave mMIMO systems [ 129 ]. Performance of iterative hybrid precoding approaches the capacity of fully digital precoding scheme [ 130 ]. “Hybrid regularized channel diagonalization” based hybrid beamforming design uses low-complex non-iterative process in a MU-mMIMO DL mmWave channel [ 131 ]. A low-complex hybrid precoding scheme called phased-ZF can approach the performance of full-complexity baseband ZF precoding in a mMIMO systems [ 132 ].

Closed-form expressions are defined to configure mobility-aware hybrid precoder for high-speed mobile users in mmWave mMIMO systems with reduced computational complexity. The number of RF chains can be activated adaptively based on channel conditions to maximize energy efficiency and beamforming gains at high SNRs [ 133 ]. Transform domain precoder is designed for a mmWave mMIMO hybrid beamforming system that reduces 60–89% of overhead and enhances 2.38–21% of spectral efficiency [ 134 ]. Fixed phase shifter implementation in mmWave hybrid precoding is hardware-efficient as it needs only less number of phase shifters with fixed and quantized phase values. A switch network (group-connected mapping) between phase shifters and antennas provides dynamic connections according to channel conditions. This setup maximizes the spectral efficiency with minimum hardware complexity [ 135 ].

4.5 CSIR Detection Algorithms in MU-ultra-mMIMO Systems

Lower capacity bounds for MRC, ZF and MMSE detection are derived. ZF and MMSE performs better than MRC Rx due to their ability to cancel intra-cell interference. In multi-cell environment, at lower power levels, cross-talk introduced by inferior MRC falls below the noise level and this Rx becomes a viable option. For example, in a small-scale Rayleigh fading channel, 50 MSs can be served with an antenna array of 100 at BS using same T–F resources. Therefore, each MS has 1bits/channel/MS and it leads to a total system throughput of 50bits/channel/MS. In this case, Tx does not know the channel instead only Rx is assumed to know the channel. Therefore, Tx sense the vector x QAM symbols, and Rx observes y . Here, the objective is to recover x from y as \({\mathbf{y}} = {\mathbf{Hx}} + {\mathbf{n}}\) .

4.5.1 ML Detector

It is an optimal detector in terms of error probability in the presence of IID gaussian noise at the Rx. We can find the best possible x by maximizing likelihood from all possible likelihoods as

Since the noise is gaussian, the estimation of x is equal to minimum of Euclidean distance between the observation y and HX for all possible x .

However, the complexity of ML detector increases exponentially with number of Tx antennas, M t .

4.5.2 ZF Detector

ZF equalizer uses inverse of channel frequency response to restore the signal at the Rx and it gives zero ISI in a noise free channel conditions. We can achieve flat fading by combining channel frequency response with ZF equalizer frequency response. This equalizer is essential when ISI is dominating the noise. For mMIMO systems, it is used when M r  ≥ M t , and there exists at least ‘M t ’ linearly independent columns in channel matrix, H . It has lower complexity than ML detector. ZF detector equalizer is given as,

where \(\left( {{\mathbf{H}}^{{\text{H}}} {\mathbf{H}}} \right)^{ - 1} {\mathbf{H}}^{{\text{H}}}\) is a pseudo inverse.

If H H H is a singular matrix, then the noise enhancement w is very high. We can perform element by element detection as z 1  = x 1  + w 1 , z 2  = x 2  + w 2 , etc., ZF equalizer can provide higher data rate gains at the cost of diversity gain. However, the challenge in ZR detector is that it needs infinitely long impulse response and also, it can amplify the noise if minimum singular value of channel matrix is too small.

4.5.3 MMSE Equalizer

In MMSE equalizer, we do not exactly recover x instead its approximation, and w noise enhancement value less. MMSE relies on Gaussian model of x . In order to avoid the noise enhancements ( w ), there is a trade-off achieved by MMSE equalizer. MMSE estimate of the transformed symbols x (where x is assumed to be Gaussian) is

where I Mt / ρ is a regularization term that depends on SNR, ρ .

If SNR is too high and approaches ‘∞’ or noise approaches zero, then MMSE approaches ZF detector. In MU-mMIMO systems, favorable propagation conditions in NLOS relies on orthogonal columns of UL channel matrix \({\mathbf{H}}^{{\text{H}}} {\mathbf{H}} \approx {\text{M}}{\mathbf{I}}_{{\text{K}}}\) , rayleigh fading entries from CN (0,1), and scaling of off-diagonal elements. The condition for LOS orthogonality is given as,

4.6 CSIT Detection Algorithms in MU-ultra-mMIMO Systems

FDD mode of channel estimation utilizes different spectrum for UL and DL. In FDD mode, the channel estimation time is proportional to number of antennas and makes the time overhead prohibitively expensive for mMIMO systems. Therefore, we have assumed the channel estimation by reciprocity, so the channel is operated in TDD mode where the same spectrum is used for both UL and DL. UL represents a “standard MIMO” communication system, so we can apply standard detection MIMO detection techniques like ZF, MMSE, maximum likelihood etc., For UL, no CSIT is needed at the users (Txs) for BS to recover the data and processing can occur in one coherence interval. UL can be trained using (B c *T c ) samples with a fixed channel where B c is coherence bandwidth, T c is coherence time. If we use a fraction a for pilots, then the rate (1 − a) x maximum rate. We want to have full spatial reuse, so some users will have same pilot. If any two users have same pilot,

w is noise that depends on length of pilot or power.

Leads to wrong precoder, loss in rate: SINR in UL and DL are interference-limited which is called “pilot contamination”.

4.6.1 Parallel Decomposition

As shown in Fig.  5 , any given complex Mr x Mt matrix can be expressed as \({\mathbf{H}} = {\mathbf{U}}\Sigma {\mathbf{V}}^{{\text{H}}}\) .where U  → unitary matrix of size M r x M t , Σ  →  Diagonal matrix of size M r x M t and rank R H  ≤ min(M r , M t ), V  → unitary matrix of size M t x M t .

Parallel decomposition using SVD

R H  = min (M r , M t ) in a rich scattering environment like Rayleigh fading environment, the value of R H is typically small in a LOS channel.

The estimated symbols,

where, x l  → data symbols, x  → transformed symbols, y  → received symbols.

It creates R H parallel channels out of which no data is transmitted on M t –R H channels. It can be combined with rate and power adaption. This is a simple detector and not affected by noise.

4.7 Capacity of MU-ultra-mMIMO Beamforming Channel

In a MIMO system, transmitting one symbol over all M t antennas leads to high diversity but with low rate. But, in a mMIMO system with beamforming as shown in Fig.  6 gives both diversity gain as well as higher data rates. The received symbols in such systems is given as

where x, y, n are scalars and select U and V along largest singular value.

mMIMO channel with beamforming

The narrowband MIMO channel can be represented as,

where \({\text{E}}\left\{ {{\mathbf{xx}}^{{\text{H}}} } \right\} = {\mathbf{R}}_{{\text{x}}} ,\) R x  → input covariance matrix and R x T  =  ρ.

\({\text{E}}\left\{ {{\mathbf{nn}}^{{\text{H}}} } \right\} = {\mathbf{I}}_{{{\text{Mr}}}}\) where n is zero mean gaussian noise.

The channel matrix, H can be represented as \({\mathbf{H}} = {\mathbf{U}}\Sigma {\mathbf{V}}^{{\text{H}}} \,{\text{with}}\,\Sigma = {\text{diag}}\left( {\sigma_{1}^{2} ,\,\sigma_{2}^{2} ,\, \ldots \sigma_{{{\text{RH}}}}^{2} ,\,0,\, \ldots 0} \right)\) .

Channel capacity for fixed channel,

The above equation maximizes overall possible covariance matrices that satisfy the total energy constraint. If the channel is unknown at the Tx (no CSIT), then the total energy is spread among all M t Tx antennas, therefore \({\mathbf{R}}_{{\text{x}}} = \left[ {{\rho \mathord{\left/ {\vphantom {\rho {{\text{Mt}}}}} \right. \kern-\nulldelimiterspace} {{\text{Mt}}}}} \right]{\mathbf{I}}_{{{\text{Mt}}}}\) .

Channel capacity,

On the other hand, if CSIT is available, we can do more sophisticated detection using SVD and exploit power allocation.

In MU-mMIMO systems users are always orthogonal and favourable propagation conditions in LOS relies on geometry of the array and far field conditions. If ‘M’ antenna elements spaced at d = λ/2 and incoming signal from angle ‘θ’, then the path difference \(\delta = {\text{d}}\sin \left( \theta \right)\) . Therefore, the received passband signals are given as follows:

and the corresponding received baseband signals:

Channel (with g k being a complex number, same for all antenna elements)

This allows beamforming in specific directions (d = λ/2).

5 Results and Discussions

3GPP recommends 28 GHz, 73 GHz, and 140 GHz frequency bands for future MU-ultra-mMIMO mmWave and THz wireless communication systems [ 136 , 137 ]. Therefore, system performance is verified at these frequencies using the simulation parameters shown in Tables  2 and 3  for a maximum of 256 × 16 MIMO order with 4 users and 8 users. At the BS, rectangular antennas array is used of size 256 (64 rows and 4 columns) is connected with 4-RF chains and at MS, 16-element (4 rows and 4 columns) square array antenna is used with 4-RF chains. With this design, every antenna element is connected to four phase shifters and radiation patterns of antenna arrays becomes isotropic with linear or rectangular geometry. In order to have peak spectral efficiencies, independent channels are assigned for each user and independent data streams are transmitted through each RF chain that leads to maximum of four data streams per user. The channel between UEs and BS is modeled as well as validated through “scattering-based MIMO spatial channel with single-bounce ray tracing” model. This model assumes the randomly placed scatters and presence of UEs at various T–R spatial locations. The MSs of each user is modeled by compensating thermal noise and path loss. Common channel is used for path loss modelling for both LOS, non-LOS scenarios, data transmissions as well as for channel sounding. The periodic updates of channel matrix at regular intervals are essential to mimic the channel variations.

From Table 4 , it is very clear that the RMS EVM values are minimum when the number of independent data streams per user are high. However, for user 1 (who has four data streams which is maximum), error values are increasing with increasing mMIMO system configurations from 64 × 16 to 256 × 16. When the number of independent data streams per user are minimum (1data stream/user, 2data stream/user), the error values are higher at lower configurations of mMIMO size, however, the RMS EVM values are reducing at higher mMIMO size configurations. From the above simulation results, we can conclude that 256 × 8, 128 × 16, and 64 × 16 mMIMO configurations are optimum for 4-user mMIMO hybrid beamforming systems when the number of independent data streams per user are 2, 4, and 4 respectively.

From Table 5 , the optimum RMS EVM values are achieved for 256 × 16 mMIMO antenna configurations where users maintain 4 independent data streams/user, and error values are highest for 64 × 4 mMIMO configuration with one independent data stream/user. For a given BS antenna arrays and modulation scheme, it is clear that RMS EVM values are decreasing when the users maintain higher number of independent data streams. For the given modulation scheme, the increasing size of BS antenna array leads to decrease in RMS EVM values. However, there are a few cases where there is a little increase RMS EVM values for increasing BS antenna array size. For users 3, 4, and 7 there is little increase in EVM values when the BS antennas are increased from 128 to 256. For user 5 there is a significant increase in EVM values when the BS antennas are increased from 64 to 256. If the user data has divided into multiple number of independent parallel data streams, then it needs less number of active antenna elements to transmit signals. Therefore, it gives trade-off between multiple independent data streams per user and size of T–R antenna arrays.

5.1 Performance of MU-mMIMO Systems for Four Users

Figures  7 and 8 represent the performance in terms of RMS EVM values in a MU-mMIMO hybrid beamforming system for four users at 140 GHz and compared with performance at 28 GHz, 73 GHz (Dilli R). Users 1,2,3, and 4 transmit their data using 4, 1, 2, and 1 number of independent data streams respectively. From the Figs.  7 and 8 , it is observed that RMS EVM values are minimum for users 1 and 3 as they maintain more number of data streams for transmission. Interestingly, for user 4, the error values are lowest at 140 GHz as compared to 28 GHz, and 73 GHz frequencies. It justifies the use of THz frequencies and higher order modulation schemes to achieve higher bandwidths, data rates and spectral efficiencies. For user 3 that maintain two independent data streams, the error values are minimum at 73 GHz and it motivates to study the performance of MU-mMIMO hybrid beamforming at these frequencies. For user 1 which has highest number of data streams, the error values are slightly increasing at 140 GHz compared to 73 GHz, and 28 GHz frequency bands, however the errors are within the acceptable limits. Also for a given order of QAM modulation scheme and carrier frequencies that are considered in this paper, the error values are slightly increasing even at higher order mMIMO antenna array configurations for the users who maintain large number of independent data streams and it needs more explorations to minimize the errors in spite that they are within limits.

Representation of RMS EVM values using 256-QAM scheme with 64 BS Antennas for four users at 28 GHz, 73 GHz, and 140 GHz frequencies

Representation of RMS EVM values using 256-QAM scheme with multiple BS Antennas for four users at 28 GHz, 73 GHz, and 140 GHz frequencies

The signal 3D-radiation patterns in MU-mMIMO hybrid beamforming systems with multiple BS antennas array sizes for 4 users are shown in Fig.  9 . The stronger lobes represent distinct data streams of users and these lobes indicate the spread achieved by hybrid beamforming. From the Fig.  9 , the observation is that radiation beams are becoming more focused for increasing antennas array size at BS and MS that enhances the reliability of signal reception there by throughput.

Signal 3D-Radiation pattern of 256-QAM with multiple BS Antennas for four users at 140 GHz

Figures  10 and 11 represent the equalized symbol constellation per data stream with various combinations of QAM modulation schemes and antennas array sizes for 4 users. From the received symbol constellation diagrams of all combinations shown, it is evident that the users with minimum number of independent data streams have higher variance in recovered symbols because of dominant modes are not present in channel and it leads to poor SNR. For the users with multiple data streams, variance of recovered data streams is very minimum as the symbol points are located with minimum dispersion in symbol constellation diagram. Fact is that the data streams use most dominant mode of scattering MIMO channel have maximum SNR values. On the other hand, symbol points in constellation diagrams of users with single data stream are highly dispersed and they have minimum SNR values.

Equalized symbol constellation per data stream of 16-QAM with 64 BS Antennas for four users at 140 GHz

Equalized symbol constellation per data stream of 16-QAM with multiple BS Antennas for four users at 140 GHz

5.2 Performance of MU-mMIMO systems for Eight Users

Figures  12 and 13 represent the performance in terms of RMS EVM values in a MU-mMIMO hybrid beamforming system for eight users at 140 GHz and compared with performance at 28 GHz, 73 GHz (Dilli R et al.,). Users 1,2,3, 4, 5, 6, 7, and 8 transmit their data using 4, 1, 3, 1, 2, 1, 3, and 1 number of independent data streams respectively. From the Figs.  12 and 13 , it is observed that RMS EVM values are minimum for users 1, 3, 5, and 7 as they maintain higher number of data streams for transmission. Interestingly, for user 1, the error values are lowest at all the frequencies as it maintains four independent data streams and the errors are further reduced at 140 GHz as compared to 28 GHz, and 73 GHz frequencies. Users who maintain three or two number of data streams have minimum errors in data transmissions at 140 GHz compared to 28GH, and 73 GHz. It justifies the use of THz frequencies and higher order modulation schemes to achieve higher bandwidths, data rates and spectral efficiencies. User 2 that maintains only one data stream has highest errors in transmission, however the alternate users who have same number of independent data streams leads to lower error values. The common observation is that alternate users with same number of independent data streams undergoes through higher transmission errors. From the overall results at 140 GHz comparing with mmWave frequencies of 28 GHz, and 73 GHz strongly recommends to explore and use the frequencies beyond 100 GHz for next-generation wireless systems.

Representation of RMS EVM values using 256-QAM scheme with 64 BS Antennas for eight users

Representation of RMS EVM values using 256-QAM scheme with multiple BS Antennas for eight users

The signal 3D-radiation patterns in MU-mMIMO hybrid beamforming systems with multiple BS antennas array sizes for 8 users are shown in Fig.  14 . The stronger lobes represent distinct data streams of users and these radiation lobes indicate the spread achieved due to hybrid beamforming. From the Fig.  14 , the observation is that radiation beams are becoming more focused for increasing antennas array size at BS and MS which leads to increase in signal reliability there by throughput.

Signal 3D-Radiation pattern of 256-QAM scheme with multiple BS Antennas for eight users at 140 GHz

Figures  15 and 16 represent the equalized symbol constellation per data stream with various combinations of QAM modulation schemes and antennas array sizes for 8 users mMIMO hybrid beamforming system. From the received symbol constellation diagrams of all combinations shown, it is evident that the variance of recovered data streams is minimum for the users with more number of independent data streams as symbol points are located with less dispersion in symbol constellation diagram. Fact is that the data streams use most dominant mode of scattering MIMO channel have maximum SNR values. But, the users with only one independent data stream have highly dispersed received symbols and it is very difficult to estimate the actual transmitted symbols. This higher variance in the received symbols is due to the fact that the absence of dominant modes in channel leads to poor SNR. However, the dispersion is very much reduced with increasing size of antenna arrays at BS and MS as shown in Figs.  15 and 16 . Therefore, it is essential to use higher order antenna arrays to achieve higher spectral efficiency.

Equalized symbol constellation per data stream of 16-QAM with 64 BS Antennas for eight users at 140 GHz

Equalized symbol constellation per data stream of 16-QAM with multiple BS Antennas for eight users at 140 GHz

6 Conclusions and Future scope

In this paper, MU-ultra-mMIMO hybrid beamforming system is designed for THz frequencies with many independent data streams per user. Rigorous analysis is performed with different QAM modulation orders as well as antenna array configurations at Tx and Rx. The results of MU-ultra-mMIMO hybrid beamforming system at THz and mmWave frequencies [ 136 ] are compared to prove the feasibility of using THz frequencies for next-generation wireless communication systems. For a 4-user mMIMO hybrid beamforming systems, it is suggested to use 256 × 8, 128 × 16, and 64 × 16 mMIMO system configurations where the number of independent data streams per user are 2,4, and 4 respectively. At the same time, for 8-user mMIMO hybrid beamforming systems, it is suggested to use 256 × 16 mMIMO antenna array configurations where users maintain four independent data streams/user. In both four-user mMIMO and eight-user mMIMO hybrid beamforming, if the user has only one independent data stream then it is essential to have higher order mMIMO antenna arrays at BS and MS in which case 256 × 4 mMIMO configuration is suitable. From the overall results of research work carried at 140 GHz, the main observation is that for users with less number of independent data streams, their RMS EVM values are higher whereas for users with more number of independent data streams, the RMS EVM values are minimum. Therefore, it is strongly recommended to maintain higher number of independent data streams per user to minimize the RMS EVM values and achieve higher order throughputs. However, hybrid beamforming designs with an objective of minimizing RMS EVM values for users with single (or less) number of independent data streams and achieve higher throughput needs to be carried out as a future research direction. Future work also includes performance analysis of the proposed MU-mMIMO hybrid beamforming system for ultra-mMIMO antenna array configurations of order 1024 × 1024 at 0.2THz, 0.34THz, and 0.4THz frequency bands as the atmospheric absorption loss beyond Frii’s free space path loss of the signal is minimum.

Data Availability

Not applicable.

Code Availability

MATLAB code is available in the supplementary material.

Abbreviations

Three dimensional

3Rd generation partnership project

Fifth generation

Sixth generation

Analog-to-digital converter

Artificial intelligence

Bit error rate

Base station

Complex number

Cyclic prefix

Channel quality information

Channel state information

Device-to-device

Electromagnetic

Error vector magnitude

Frequency division duplex

Fast fourier transform

Global position system

Hybrid beamforming with peak search

Independent and identically distributed

Industrial IoT

Internet of everything

Internet of space

Internet of things

Inter symbol interference

Joint spatial division multiplexing

Light detection and ranging

Line of sight

• Multi–input–multi–output
• Massive MIMO

Minimum mean-square error

Maximal ratio combining

Mobile station

Mean square error

Non-line of sight

Non-orthogonal Multiple Access

Orthogonal frequency division multiplexing

Orthogonal matching pursuit

Quadrature amplitude modulation

Quality of service

Radio frequency

Root mean square

Signal-to-interference-plus-noise ratio

Signal-to-noise ratio

Time division duplex

Time–frequency

Transmit-receive

Transmitter

User equipment

Zero forcing

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Dilli, R. Design and Feasibility Verification of 6G Wireless Communication Systems with State of the Art Technologies. Int J Wireless Inf Networks 29 , 93–117 (2022). https://doi.org/10.1007/s10776-021-00546-3

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Study and Investigation on 5G Technology: A Systematic Review

Ramraj dangi.

1 School of Computing Science and Engineering, VIT University Bhopal, Bhopal 466114, India; [email protected] (R.D.); [email protected] (P.L.)

Praveen Lalwani

Gaurav choudhary.

2 Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark; moc.liamg@7777yrahduohcvaruag

3 Department of Information Security Engineering, Soonchunhyang University, Asan-si 31538, Korea

Giovanni Pau

4 Faculty of Engineering and Architecture, Kore University of Enna, 94100 Enna, Italy; [email protected]

Associated Data

Not applicable.

In wireless communication, Fifth Generation (5G) Technology is a recent generation of mobile networks. In this paper, evaluations in the field of mobile communication technology are presented. In each evolution, multiple challenges were faced that were captured with the help of next-generation mobile networks. Among all the previously existing mobile networks, 5G provides a high-speed internet facility, anytime, anywhere, for everyone. 5G is slightly different due to its novel features such as interconnecting people, controlling devices, objects, and machines. 5G mobile system will bring diverse levels of performance and capability, which will serve as new user experiences and connect new enterprises. Therefore, it is essential to know where the enterprise can utilize the benefits of 5G. In this research article, it was observed that extensive research and analysis unfolds different aspects, namely, millimeter wave (mmWave), massive multiple-input and multiple-output (Massive-MIMO), small cell, mobile edge computing (MEC), beamforming, different antenna technology, etc. This article’s main aim is to highlight some of the most recent enhancements made towards the 5G mobile system and discuss its future research objectives.

1. Introduction

Most recently, in three decades, rapid growth was marked in the field of wireless communication concerning the transition of 1G to 4G [ 1 , 2 ]. The main motto behind this research was the requirements of high bandwidth and very low latency. 5G provides a high data rate, improved quality of service (QoS), low-latency, high coverage, high reliability, and economically affordable services. 5G delivers services categorized into three categories: (1) Extreme mobile broadband (eMBB). It is a nonstandalone architecture that offers high-speed internet connectivity, greater bandwidth, moderate latency, UltraHD streaming videos, virtual reality and augmented reality (AR/VR) media, and many more. (2) Massive machine type communication (eMTC), 3GPP releases it in its 13th specification. It provides long-range and broadband machine-type communication at a very cost-effective price with less power consumption. eMTC brings a high data rate service, low power, extended coverage via less device complexity through mobile carriers for IoT applications. (3) ultra-reliable low latency communication (URLLC) offers low-latency and ultra-high reliability, rich quality of service (QoS), which is not possible with traditional mobile network architecture. URLLC is designed for on-demand real-time interaction such as remote surgery, vehicle to vehicle (V2V) communication, industry 4.0, smart grids, intelligent transport system, etc. [ 3 ].

1.1. Evolution from 1G to 5G

First generation (1G): 1G cell phone was launched between the 1970s and 80s, based on analog technology, which works just like a landline phone. It suffers in various ways, such as poor battery life, voice quality, and dropped calls. In 1G, the maximum achievable speed was 2.4 Kbps.

Second Generation (2G): In 2G, the first digital system was offered in 1991, providing improved mobile voice communication over 1G. In addition, Code-Division Multiple Access (CDMA) and Global System for Mobile (GSM) concepts were also discussed. In 2G, the maximum achievable speed was 1 Mpbs.

Third Generation (3G): When technology ventured from 2G GSM frameworks into 3G universal mobile telecommunication system (UMTS) framework, users encountered higher system speed and quicker download speed making constant video calls. 3G was the first mobile broadband system that was formed to provide the voice with some multimedia. The technology behind 3G was high-speed packet access (HSPA/HSPA+). 3G used MIMO for multiplying the power of the wireless network, and it also used packet switching for fast data transmission.

Fourth Generation (4G): It is purely mobile broadband standard. In digital mobile communication, it was observed information rate that upgraded from 20 to 60 Mbps in 4G [ 4 ]. It works on LTE and WiMAX technologies, as well as provides wider bandwidth up to 100 Mhz. It was launched in 2010.

Fourth Generation LTE-A (4.5G): It is an advanced version of standard 4G LTE. LTE-A uses MIMO technology to combine multiple antennas for both transmitters as well as a receiver. Using MIMO, multiple signals and multiple antennas can work simultaneously, making LTE-A three times faster than standard 4G. LTE-A offered an improved system limit, decreased deferral in the application server, access triple traffic (Data, Voice, and Video) wirelessly at any time anywhere in the world.LTE-A delivers speeds of over 42 Mbps and up to 90 Mbps.

Fifth Generation (5G): 5G is a pillar of digital transformation; it is a real improvement on all the previous mobile generation networks. 5G brings three different services for end user like Extreme mobile broadband (eMBB). It offers high-speed internet connectivity, greater bandwidth, moderate latency, UltraHD streaming videos, virtual reality and augmented reality (AR/VR) media, and many more. Massive machine type communication (eMTC), it provides long-range and broadband machine-type communication at a very cost-effective price with less power consumption. eMTC brings a high data rate service, low power, extended coverage via less device complexity through mobile carriers for IoT applications. Ultra-reliable low latency communication (URLLC) offers low-latency and ultra-high reliability, rich quality of service (QoS), which is not possible with traditional mobile network architecture. URLLC is designed for on-demand real-time interaction such as remote surgery, vehicle to vehicle (V2V) communication, industry 4.0, smart grids, intelligent transport system, etc. 5G faster than 4G and offers remote-controlled operation over a reliable network with zero delays. It provides down-link maximum throughput of up to 20 Gbps. In addition, 5G also supports 4G WWWW (4th Generation World Wide Wireless Web) [ 5 ] and is based on Internet protocol version 6 (IPv6) protocol. 5G provides unlimited internet connection at your convenience, anytime, anywhere with extremely high speed, high throughput, low-latency, higher reliability and scalability, and energy-efficient mobile communication technology [ 6 ]. 5G mainly divided in two parts 6 GHz 5G and Millimeter wave(mmWave) 5G.

6 GHz is a mid frequency band which works as a mid point between capacity and coverage to offer perfect environment for 5G connectivity. 6 GHz spectrum will provide high bandwidth with improved network performance. It offers continuous channels that will reduce the need for network densification when mid-band spectrum is not available and it makes 5G connectivity affordable at anytime, anywhere for everyone.

mmWave is an essential technology of 5G network which build high performance network. 5G mmWave offer diverse services that is why all network providers should add on this technology in their 5G deployment planning. There are lots of service providers who deployed 5G mmWave, and their simulation result shows that 5G mmwave is a far less used spectrum. It provides very high speed wireless communication and it also offers ultra-wide bandwidth for next generation mobile network.

The evolution of wireless mobile technologies are presented in Table 1 . The abbreviations used in this paper are mentioned in Table 2 .

Summary of Mobile Technology.

Table of Notations and Abbreviations.

1.2. Key Contributions

The objective of this survey is to provide a detailed guide of 5G key technologies, methods to researchers, and to help with understanding how the recent works addressed 5G problems and developed solutions to tackle the 5G challenges; i.e., what are new methods that must be applied and how can they solve problems? Highlights of the research article are as follows.

• This survey focused on the recent trends and development in the era of 5G and novel contributions by the researcher community and discussed technical details on essential aspects of the 5G advancement.
• In this paper, the evolution of the mobile network from 1G to 5G is presented. In addition, the growth of mobile communication under different attributes is also discussed.
• This paper covers the emerging applications and research groups working on 5G & different research areas in 5G wireless communication network with a descriptive taxonomy.
• This survey discusses the current vision of the 5G networks, advantages, applications, key technologies, and key features. Furthermore, machine learning prospects are also explored with the emerging requirements in the 5G era. The article also focused on technical aspects of 5G IoT Based approaches and optimization techniques for 5G.
• we provide an extensive overview and recent advancement of emerging technologies of 5G mobile network, namely, MIMO, Non-Orthogonal Multiple Access (NOMA), mmWave, Internet of Things (IoT), Machine Learning (ML), and optimization. Also, a technical summary is discussed by highlighting the context of current approaches and corresponding challenges.
• Security challenges and considerations while developing 5G technology are discussed.
• Finally, the paper concludes with the future directives.

The existing survey focused on architecture, key concepts, and implementation challenges and issues. In contrast, this survey covers the state-of-the-art techniques as well as corresponding recent novel developments by researchers. Various recent significant papers are discussed with the key technologies accelerating the development and production of 5G products.

2. Existing Surveys and Their Applicability

In this paper, a detailed survey on various technologies of 5G networks is presented. Various researchers have worked on different technologies of 5G networks. In this section, Table 3 gives a tabular representation of existing surveys of 5G networks. Massive MIMO, NOMA, small cell, mmWave, beamforming, and MEC are the six main pillars that helped to implement 5G networks in real life.

A comparative overview of existing surveys on different technologies of 5G networks.

2.1. Limitations of Existing Surveys

The existing survey focused on architecture, key concepts, and implementation challenges and issues. The numerous current surveys focused on various 5G technologies with different parameters, and the authors did not cover all the technologies of the 5G network in detail with challenges and recent advancements. Few authors worked on MIMO (Non-Orthogonal Multiple Access) NOMA, MEC, small cell technologies. In contrast, some others worked on beamforming, Millimeter-wave (mmWave). But the existing survey did not cover all the technologies of the 5G network from a research and advancement perspective. No detailed survey is available in the market covering all the 5G network technologies and currently published research trade-offs. So, our main aim is to give a detailed study of all the technologies working on the 5G network. In contrast, this survey covers the state-of-the-art techniques as well as corresponding recent novel developments by researchers. Various recent significant papers are discussed with the key technologies accelerating the development and production of 5G products. This survey article collected key information about 5G technology and recent advancements, and it can be a kind of a guide for the reader. This survey provides an umbrella approach to bring multiple solutions and recent improvements in a single place to accelerate the 5G research with the latest key enabling solutions and reviews. A systematic layout representation of the survey in Figure 1 . We provide a state-of-the-art comparative overview of the existing surveys on different technologies of 5G networks in Table 3 .

Systematic layout representation of survey.

2.2. Article Organization

This article is organized under the following sections. Section 2 presents existing surveys and their applicability. In Section 3 , the preliminaries of 5G technology are presented. In Section 4 , recent advances of 5G technology based on Massive MIMO, NOMA, Millimeter Wave, 5G with IoT, machine learning for 5G, and Optimization in 5G are provided. In Section 5 , a description of novel 5G features over 4G is provided. Section 6 covered all the security concerns of the 5G network. Section 7 , 5G technology based on above-stated challenges summarize in tabular form. Finally, Section 8 and Section 9 conclude the study, which paves the path for future research.

3. Preliminary Section

3.1. emerging 5g paradigms and its features.

5G provides very high speed, low latency, and highly salable connectivity between multiple devices and IoT worldwide. 5G will provide a very flexible model to develop a modern generation of applications and industry goals [ 26 , 27 ]. There are many services offered by 5G network architecture are stated below:

Massive machine to machine communications: 5G offers novel, massive machine-to-machine communications [ 28 ], also known as the IoT [ 29 ], that provide connectivity between lots of machines without any involvement of humans. This service enhances the applications of 5G and provides connectivity between agriculture, construction, and industries [ 30 ].

Ultra-reliable low latency communications (URLLC): This service offers real-time management of machines, high-speed vehicle-to-vehicle connectivity, industrial connectivity and security principles, and highly secure transport system, and multiple autonomous actions. Low latency communications also clear up a different area where remote medical care, procedures, and operation are all achievable [ 31 ].

Enhanced mobile broadband: Enhance mobile broadband is an important use case of 5G system, which uses massive MIMO antenna, mmWave, beamforming techniques to offer very high-speed connectivity across a wide range of areas [ 32 ].

For communities: 5G provides a very flexible internet connection between lots of machines to make smart homes, smart schools, smart laboratories, safer and smart automobiles, and good health care centers [ 33 ].

For businesses and industry: As 5G works on higher spectrum ranges from 24 to 100 GHz. This higher frequency range provides secure low latency communication and high-speed wireless connectivity between IoT devices and industry 4.0, which opens a market for end-users to enhance their business models [ 34 ].

New and Emerging technologies: As 5G came up with many new technologies like beamforming, massive MIMO, mmWave, small cell, NOMA, MEC, and network slicing, it introduced many new features to the market. Like virtual reality (VR), users can experience the physical presence of people who are millions of kilometers away from them. Many new technologies like smart homes, smart workplaces, smart schools, smart sports academy also came into the market with this 5G Mobile network model [ 35 ].

3.2. Commercial Service Providers of 5G

5G provides high-speed internet browsing, streaming, and downloading with very high reliability and low latency. 5G network will change your working style, and it will increase new business opportunities and provide innovations that we cannot imagine. This section covers top service providers of 5G network [ 36 , 37 ].

Ericsson: Ericsson is a Swedish multinational networking and telecommunications company, investing around 25.62 billion USD in 5G network, which makes it the biggest telecommunication company. It claims that it is the only company working on all the continents to make the 5G network a global standard for the next generation wireless communication. Ericsson developed the first 5G radio prototype that enables the operators to set up the live field trials in their network, which helps operators understand how 5G reacts. It plays a vital role in the development of 5G hardware. It currently provides 5G services in over 27 countries with content providers like China Mobile, GCI, LGU+, AT&T, Rogers, and many more. It has 100 commercial agreements with different operators as of 2020.

Verizon: It is American multinational telecommunication which was founded in 1983. Verizon started offering 5G services in April 2020, and by December 2020, it has actively provided 5G services in 30 cities of the USA. They planned that by the end of 2021, they would deploy 5G in 30 more new cities. Verizon deployed a 5G network on mmWave, a very high band spectrum between 30 to 300 GHz. As it is a significantly less used spectrum, it provides very high-speed wireless communication. MmWave offers ultra-wide bandwidth for next-generation mobile networks. MmWave is a faster and high-band spectrum that has a limited range. Verizon planned to increase its number of 5G cells by 500% by 2020. Verizon also has an ultra wide-band flagship 5G service which is the best 5G service that increases the market price of Verizon.

Nokia: Nokia is a Finnish multinational telecommunications company which was founded in 1865. Nokia is one of the companies which adopted 5G technology very early. It is developing, researching, and building partnerships with various 5G renders to offer 5G communication as soon as possible. Nokia collaborated with Deutsche Telekom and Hamburg Port Authority and provided them 8000-hectare site for their 5G MoNArch project. Nokia is the only company that supplies 5G technology to all the operators of different countries like AT&T, Sprint, T-Mobile US and Verizon in the USA, Korea Telecom, LG U+ and SK Telecom in South Korea and NTT DOCOMO, KDDI, and SoftBank in Japan. Presently, Nokia has around 150+ agreements and 29 live networks all over the world. Nokia is continuously working hard on 5G technology to expand 5G networks all over the globe.

AT&T: AT&T is an American multinational company that was the first to deploy a 5G network in reality in 2018. They built a gigabit 5G network connection in Waco, TX, Kalamazoo, MI, and South Bend to achieve this. It is the first company that archives 1–2 gigabit per second speed in 2019. AT&T claims that it provides a 5G network connection among 225 million people worldwide by using a 6 GHz spectrum band.

T-Mobile: T-Mobile US (TMUS) is an American wireless network operator which was the first service provider that offers a real 5G nationwide network. The company knew that high-band 5G was not feasible nationwide, so they used a 600 MHz spectrum to build a significant portion of its 5G network. TMUS is planning that by 2024 they will double the total capacity and triple the full 5G capacity of T-Mobile and Sprint combined. The sprint buyout is helping T-Mobile move forward the company’s current market price to 129.98 USD.

Samsung: Samsung started their research in 5G technology in 2011. In 2013, Samsung successfully developed the world’s first adaptive array transceiver technology operating in the millimeter-wave Ka bands for cellular communications. Samsung provides several hundred times faster data transmission than standard 4G for core 5G mobile communication systems. The company achieved a lot of success in the next generation of technology, and it is considered one of the leading companies in the 5G domain.

Qualcomm: Qualcomm is an American multinational corporation in San Diego, California. It is also one of the leading company which is working on 5G chip. Qualcomm’s first 5G modem chip was announced in October 2016, and a prototype was demonstrated in October 2017. Qualcomm mainly focuses on building products while other companies talk about 5G; Qualcomm is building the technologies. According to one magazine, Qualcomm was working on three main areas of 5G networks. Firstly, radios that would use bandwidth from any network it has access to; secondly, creating more extensive ranges of spectrum by combining smaller pieces; and thirdly, a set of services for internet applications.

ZTE Corporation: ZTE Corporation was founded in 1985. It is a partially Chinese state-owned technology company that works in telecommunication. It was a leading company that worked on 4G LTE, and it is still maintaining its value and doing research and tests on 5G. It is the first company that proposed Pre5G technology with some series of solutions.

NEC Corporation: NEC Corporation is a Japanese multinational information technology and electronics corporation headquartered in Minato, Tokyo. ZTE also started their research on 5G, and they introduced a new business concept. NEC’s main aim is to develop 5G NR for the global mobile system and create secure and intelligent technologies to realize 5G services.

Cisco: Cisco is a USA networking hardware company that also sleeves up for 5G network. Cisco’s primary focus is to support 5G in three ways: Service—enable 5G services faster so all service providers can increase their business. Infrastructure—build 5G-oriented infrastructure to implement 5G more quickly. Automation—make a more scalable, flexible, and reliable 5G network. The companies know the importance of 5G, and they want to connect more than 30 billion devices in the next couple of years. Cisco intends to work on network hardening as it is a vital part of 5G network. Cisco used AI with deep learning to develop a 5G Security Architecture, enabling Secure Network Transformation.

3.3. 5G Research Groups

Many research groups from all over the world are working on a 5G wireless mobile network [ 38 ]. These groups are continuously working on various aspects of 5G. The list of those research groups are presented as follows: 5GNOW (5th Generation Non-Orthogonal Waveform for Asynchronous Signaling), NEWCOM (Network of Excellence in Wireless Communication), 5GIC (5G Innovation Center), NYU (New York University) Wireless, 5GPPP (5G Infrastructure Public-Private Partnership), EMPHATIC (Enhanced Multi-carrier Technology for Professional Adhoc and Cell-Based Communication), ETRI(Electronics and Telecommunication Research Institute), METIS (Mobile and wireless communication Enablers for the Twenty-twenty Information Society) [ 39 ]. The various research groups along with the research area are presented in Table 4 .

Research groups working on 5G mobile networks.

3.4. 5G Applications

5G is faster than 4G and offers remote-controlled operation over a reliable network with zero delays. It provides down-link maximum throughput of up to 20 Gbps. In addition, 5G also supports 4G WWWW (4th Generation World Wide Wireless Web) [ 5 ] and is based on Internet protocol version 6 (IPv6) protocol. 5G provides unlimited internet connection at your convenience, anytime, anywhere with extremely high speed, high throughput, low-latency, higher reliability, greater scalablility, and energy-efficient mobile communication technology [ 6 ].

There are lots of applications of 5G mobile network are as follows:

• High-speed mobile network: 5G is an advancement on all the previous mobile network technologies, which offers very high speed downloading speeds 0 of up to 10 to 20 Gbps. The 5G wireless network works as a fiber optic internet connection. 5G is different from all the conventional mobile transmission technologies, and it offers both voice and high-speed data connectivity efficiently. 5G offers very low latency communication of less than a millisecond, useful for autonomous driving and mission-critical applications. 5G will use millimeter waves for data transmission, providing higher bandwidth and a massive data rate than lower LTE bands. As 5 Gis a fast mobile network technology, it will enable virtual access to high processing power and secure and safe access to cloud services and enterprise applications. Small cell is one of the best features of 5G, which brings lots of advantages like high coverage, high-speed data transfer, power saving, easy and fast cloud access, etc. [ 40 ].
• Entertainment and multimedia: In one analysis in 2015, it was found that more than 50 percent of mobile internet traffic was used for video downloading. This trend will surely increase in the future, which will make video streaming more common. 5G will offer High-speed streaming of 4K videos with crystal clear audio, and it will make a high definition virtual world on your mobile. 5G will benefit the entertainment industry as it offers 120 frames per second with high resolution and higher dynamic range video streaming, and HD TV channels can also be accessed on mobile devices without any interruptions. 5G provides low latency high definition communication so augmented reality (AR), and virtual reality (VR) will be very easily implemented in the future. Virtual reality games are trendy these days, and many companies are investing in HD virtual reality games. The 5G network will offer high-speed internet connectivity with a better gaming experience [ 41 ].
• Smart homes : smart home appliances and products are in demand these days. The 5G network makes smart homes more real as it offers high-speed connectivity and monitoring of smart appliances. Smart home appliances are easily accessed and configured from remote locations using the 5G network as it offers very high-speed low latency communication.
• Smart cities: 5G wireless network also helps develop smart cities applications such as automatic traffic management, weather update, local area broadcasting, energy-saving, efficient power supply, smart lighting system, water resource management, crowd management, emergency control, etc.
• Industrial IoT: 5G wireless technology will provide lots of features for future industries such as safety, process tracking, smart packing, shipping, energy efficiency, automation of equipment, predictive maintenance, and logistics. 5G smart sensor technology also offers smarter, safer, cost-effective, and energy-saving industrial IoT operations.
• Smart Farming: 5G technology will play a crucial role in agriculture and smart farming. 5G sensors and GPS technology will help farmers track live attacks on crops and manage them quickly. These smart sensors can also be used for irrigation, pest, insect, and electricity control.
• Autonomous Driving: The 5G wireless network offers very low latency high-speed communication, significant for autonomous driving. It means self-driving cars will come to real life soon with 5G wireless networks. Using 5G autonomous cars can easily communicate with smart traffic signs, objects, and other vehicles running on the road. 5G’s low latency feature makes self-driving more real as every millisecond is essential for autonomous vehicles, decision-making is done in microseconds to avoid accidents.
• Healthcare and mission-critical applications: 5G technology will bring modernization in medicine where doctors and practitioners can perform advanced medical procedures. The 5G network will provide connectivity between all classrooms, so attending seminars and lectures will be easier. Through 5G technology, patients can connect with doctors and take their advice. Scientists are building smart medical devices which can help people with chronic medical conditions. The 5G network will boost the healthcare industry with smart devices, the internet of medical things, smart sensors, HD medical imaging technologies, and smart analytics systems. 5G will help access cloud storage, so accessing healthcare data will be very easy from any location worldwide. Doctors and medical practitioners can easily store and share large files like MRI reports within seconds using the 5G network.
• Satellite Internet: In many remote areas, ground base stations are not available, so 5G will play a crucial role in providing connectivity in such areas. The 5G network will provide connectivity using satellite systems, and the satellite system uses a constellation of multiple small satellites to provide connectivity in urban and rural areas across the world.

4. 5G Technologies

This section describes recent advances of 5G Massive MIMO, 5G NOMA, 5G millimeter wave, 5G IOT, 5G with machine learning, and 5G optimization-based approaches. In addition, the summary is also presented in each subsection that paves the researchers for the future research direction.

4.1. 5G Massive MIMO

Multiple-input-multiple-out (MIMO) is a very important technology for wireless systems. It is used for sending and receiving multiple signals simultaneously over the same radio channel. MIMO plays a very big role in WI-FI, 3G, 4G, and 4G LTE-A networks. MIMO is mainly used to achieve high spectral efficiency and energy efficiency but it was not up to the mark MIMO provides low throughput and very low reliable connectivity. To resolve this, lots of MIMO technology like single user MIMO (SU-MIMO), multiuser MIMO (MU-MIMO) and network MIMO were used. However, these new MIMO also did not still fulfill the demand of end users. Massive MIMO is an advancement of MIMO technology used in the 5G network in which hundreds and thousands of antennas are attached with base stations to increase throughput and spectral efficiency. Multiple transmit and receive antennas are used in massive MIMO to increase the transmission rate and spectral efficiency. When multiple UEs generate downlink traffic simultaneously, massive MIMO gains higher capacity. Massive MIMO uses extra antennas to move energy into smaller regions of space to increase spectral efficiency and throughput [ 43 ]. In traditional systems data collection from smart sensors is a complex task as it increases latency, reduced data rate and reduced reliability. While massive MIMO with beamforming and huge multiplexing techniques can sense data from different sensors with low latency, high data rate and higher reliability. Massive MIMO will help in transmitting the data in real-time collected from different sensors to central monitoring locations for smart sensor applications like self-driving cars, healthcare centers, smart grids, smart cities, smart highways, smart homes, and smart enterprises [ 44 ].

Highlights of 5G Massive MIMO technology are as follows:

• Data rate: Massive MIMO is advised as the one of the dominant technologies to provide wireless high speed and high data rate in the gigabits per seconds.
• The relationship between wave frequency and antenna size: Both are inversely proportional to each other. It means lower frequency signals need a bigger antenna and vise versa.

Pictorial representation of multi-input and multi-output (MIMO).

• MIMO role in 5G: Massive MIMO will play a crucial role in the deployment of future 5G mobile communication as greater spectral and energy efficiency could be enabled.

State-of-the-Art Approaches

Plenty of approaches were proposed to resolve the issues of conventional MIMO [ 7 ].

The MIMO multirate, feed-forward controller is suggested by Mae et al. [ 46 ]. In the simulation, the proposed model generates the smooth control input, unlike the conventional MIMO, which generates oscillated control inputs. It also outperformed concerning the error rate. However, a combination of multirate and single rate can be used for better results.

The performance of stand-alone MIMO, distributed MIMO with and without corporation MIMO, was investigated by Panzner et al. [ 47 ]. In addition, an idea about the integration of large scale in the 5G technology was also presented. In the experimental analysis, different MIMO configurations are considered. The variation in the ratio of overall transmit antennas to spatial is deemed step-wise from equality to ten.

The simulation of massive MIMO noncooperative and cooperative systems for down-link behavior was performed by He et al. [ 48 ]. It depends on present LTE systems, which deal with various antennas in the base station set-up. It was observed that collaboration in different BS improves the system behaviors, whereas throughput is reduced slightly in this approach. However, a new method can be developed which can enhance both system behavior and throughput.

In [ 8 ], different approaches that increased the energy efficiency benefits provided by massive MIMO were presented. They analyzed the massive MIMO technology and described the detailed design of the energy consumption model for massive MIMO systems. This article has explored several techniques to enhance massive MIMO systems’ energy efficiency (EE) gains. This paper reviews standard EE-maximization approaches for the conventional massive MIMO systems, namely, scaling number of antennas, real-time implementing low-complexity operations at the base station (BS), power amplifier losses minimization, and radio frequency (RF) chain minimization requirements. In addition, open research direction is also identified.

In [ 49 ], various existing approaches based on different antenna selection and scheduling, user selection and scheduling, and joint antenna and user scheduling methods adopted in massive MIMO systems are presented in this paper. The objective of this survey article was to make awareness about the current research and future research direction in MIMO for systems. They analyzed that complete utilization of resources and bandwidth was the most crucial factor which enhances the sum rate.

In [ 50 ], authors discussed the development of various techniques for pilot contamination. To calculate the impact of pilot contamination in time division duplex (TDD) massive MIMO system, TDD and frequency division duplexing FDD patterns in massive MIMO techniques are used. They discussed different issues in pilot contamination in TDD massive MIMO systems with all the possible future directions of research. They also classified various techniques to generate the channel information for both pilot-based and subspace-based approaches.

In [ 19 ], the authors defined the uplink and downlink services for a massive MIMO system. In addition, it maintains a performance matrix that measures the impact of pilot contamination on different performances. They also examined the various application of massive MIMO such as small cells, orthogonal frequency-division multiplexing (OFDM) schemes, massive MIMO IEEE 802, 3rd generation partnership project (3GPP) specifications, and higher frequency bands. They considered their research work crucial for cutting edge massive MIMO and covered many issues like system throughput performance and channel state acquisition at higher frequencies.

In [ 13 ], various approaches were suggested for MIMO future generation wireless communication. They made a comparative study based on performance indicators such as peak data rate, energy efficiency, latency, throughput, etc. The key findings of this survey are as follows: (1) spatial multiplexing improves the energy efficiency; (2) design of MIMO play a vital role in the enhancement of throughput; (3) enhancement of mMIMO focusing on energy & spectral performance; (4) discussed the future challenges to improve the system design.

In [ 51 ], the study of large-scale MIMO systems for an energy-efficient system sharing method was presented. For the resource allocation, circuit energy and transmit energy expenditures were taken into consideration. In addition, the optimization techniques were applied for an energy-efficient resource sharing system to enlarge the energy efficiency for individual QoS and energy constraints. The author also examined the BS configuration, which includes homogeneous and heterogeneous UEs. While simulating, they discussed that the total number of transmit antennas plays a vital role in boosting energy efficiency. They highlighted that the highest energy efficiency was obtained when the BS was set up with 100 antennas that serve 20 UEs.

This section includes various works done on 5G MIMO technology by different author’s. Table 5 shows how different author’s worked on improvement of various parameters such as throughput, latency, energy efficiency, and spectral efficiency with 5G MIMO technology.

Summary of massive MIMO-based approaches in 5G technology.

4.2. 5G Non-Orthogonal Multiple Access (NOMA)

NOMA is a very important radio access technology used in next generation wireless communication. Compared to previous orthogonal multiple access techniques, NOMA offers lots of benefits like high spectrum efficiency, low latency with high reliability and high speed massive connectivity. NOMA mainly works on a baseline to serve multiple users with the same resources in terms of time, space and frequency. NOMA is mainly divided into two main categories one is code domain NOMA and another is power domain NOMA. Code-domain NOMA can improve the spectral efficiency of mMIMO, which improves the connectivity in 5G wireless communication. Code-domain NOMA was divided into some more multiple access techniques like sparse code multiple access, lattice-partition multiple access, multi-user shared access and pattern-division multiple access [ 52 ]. Power-domain NOMA is widely used in 5G wireless networks as it performs well with various wireless communication techniques such as MIMO, beamforming, space-time coding, network coding, full-duplex and cooperative communication etc. [ 53 ]. The conventional orthogonal frequency-division multiple access (OFDMA) used by 3GPP in 4G LTE network provides very low spectral efficiency when bandwidth resources are allocated to users with low channel state information (CSI). NOMA resolved this issue as it enables users to access all the subcarrier channels so bandwidth resources allocated to the users with low CSI can still be accessed by the users with strong CSI which increases the spectral efficiency. The 5G network will support heterogeneous architecture in which small cell and macro base stations work for spectrum sharing. NOMA is a key technology of the 5G wireless system which is very helpful for heterogeneous networks as multiple users can share their data in a small cell using the NOMA principle.The NOMA is helpful in various applications like ultra-dense networks (UDN), machine to machine (M2M) communication and massive machine type communication (mMTC). As NOMA provides lots of features it has some challenges too such as NOMA needs huge computational power for a large number of users at high data rates to run the SIC algorithms. Second, when users are moving from the networks, to manage power allocation optimization is a challenging task for NOMA [ 54 ]. Hybrid NOMA (HNOMA) is a combination of power-domain and code-domain NOMA. HNOMA uses both power differences and orthogonal resources for transmission among multiple users. As HNOMA is using both power-domain NOMA and code-domain NOMA it can achieve higher spectral efficiency than Power-domain NOMA and code-domain NOMA. In HNOMA multiple groups can simultaneously transmit signals at the same time. It uses a message passing algorithm (MPA) and successive interference cancellation (SIC)-based detection at the base station for these groups [ 55 ].

Highlights of 5G NOMA technology as follows:

Pictorial representation of orthogonal and Non-Orthogonal Multiple Access (NOMA).

• NOMA provides higher data rates and resolves all the loop holes of OMA that makes 5G mobile network more scalable and reliable.
• As multiple users use same frequency band simultaneously it increases the performance of whole network.
• To setup intracell and intercell interference NOMA provides nonorthogonal transmission on the transmitter end.
• The primary fundamental of NOMA is to improve the spectrum efficiency by strengthening the ramification of receiver.

State-of-the-Art of Approaches

A plenty of approaches were developed to address the various issues in NOMA.

A novel approach to address the multiple receiving signals at the same frequency is proposed in [ 22 ]. In NOMA, multiple users use the same sub-carrier, which improves the fairness and throughput of the system. As a nonorthogonal method is used among multiple users, at the time of retrieving the user’s signal at the receiver’s end, joint processing is required. They proposed solutions to optimize the receiver and the radio resource allocation of uplink NOMA. Firstly, the authors proposed an iterative MUDD which utilizes the information produced by the channel decoder to improve the performance of the multiuser detector. After that, the author suggested a power allocation and novel subcarrier that enhances the users’ weighted sum rate for the NOMA scheme. Their proposed model showed that NOMA performed well as compared to OFDM in terms of fairness and efficiency.

In [ 53 ], the author’s reviewed a power-domain NOMA that uses superposition coding (SC) and successive interference cancellation (SIC) at the transmitter and the receiver end. Lots of analyses were held that described that NOMA effectively satisfies user data rate demands and network-level of 5G technologies. The paper presented a complete review of recent advances in the 5G NOMA system. It showed the comparative analysis regarding allocation procedures, user fairness, state-of-the-art efficiency evaluation, user pairing pattern, etc. The study also analyzes NOMA’s behavior when working with other wireless communication techniques, namely, beamforming, MIMO, cooperative connections, network, space-time coding, etc.

In [ 9 ], the authors proposed NOMA with MEC, which improves the QoS as well as reduces the latency of the 5G wireless network. This model increases the uplink NOMA by decreasing the user’s uplink energy consumption. They formulated an optimized NOMA framework that reduces the energy consumption of MEC by using computing and communication resource allocation, user clustering, and transmit powers.

In [ 10 ], the authors proposed a model which investigates outage probability under average channel state information CSI and data rate in full CSI to resolve the problem of optimal power allocation, which increase the NOMA downlink system among users. They developed simple low-complexity algorithms to provide the optimal solution. The obtained simulation results showed NOMA’s efficiency, achieving higher performance fairness compared to the TDMA configurations. It was observed from the results that NOMA, through the appropriate power amplifiers (PA), ensures the high-performance fairness requirement for the future 5G wireless communication networks.

In [ 56 ], researchers discussed that the NOMA technology and waveform modulation techniques had been used in the 5G mobile network. Therefore, this research gave a detailed survey of non-orthogonal waveform modulation techniques and NOMA schemes for next-generation mobile networks. By analyzing and comparing multiple access technologies, they considered the future evolution of these technologies for 5G mobile communication.

In [ 57 ], the authors surveyed non-orthogonal multiple access (NOMA) from the development phase to the recent developments. They have also compared NOMA techniques with traditional OMA techniques concerning information theory. The author discussed the NOMA schemes categorically as power and code domain, including the design principles, operating principles, and features. Comparison is based upon the system’s performance, spectral efficiency, and the receiver’s complexity. Also discussed are the future challenges, open issues, and their expectations of NOMA and how it will support the key requirements of 5G mobile communication systems with massive connectivity and low latency.

In [ 17 ], authors present the first review of an elementary NOMA model with two users, which clarify its central precepts. After that, a general design with multicarrier supports with a random number of users on each sub-carrier is analyzed. In performance evaluation with the existing approaches, resource sharing and multiple-input multiple-output NOMA are examined. Furthermore, they took the key elements of NOMA and its potential research demands. Finally, they reviewed the two-user SC-NOMA design and a multi-user MC-NOMA design to highlight NOMA’s basic approaches and conventions. They also present the research study about the performance examination, resource assignment, and MIMO in NOMA.

In this section, various works by different authors done on 5G NOMA technology is covered. Table 6 shows how other authors worked on the improvement of various parameters such as spectral efficiency, fairness, and computing capacity with 5G NOMA technology.

Summary of NOMA-based approaches in 5G technology.

4.3. 5G Millimeter Wave (mmWave)

Millimeter wave is an extremely high frequency band, which is very useful for 5G wireless networks. MmWave uses 30 GHz to 300 GHz spectrum band for transmission. The frequency band between 30 GHz to 300 GHz is known as mmWave because these waves have wavelengths between 1 to 10 mm. Till now radar systems and satellites are only using mmWave as these are very fast frequency bands which provide very high speed wireless communication. Many mobile network providers also started mmWave for transmitting data between base stations. Using two ways the speed of data transmission can be improved one is by increasing spectrum utilization and second is by increasing spectrum bandwidth. Out of these two approaches increasing bandwidth is quite easy and better. The frequency band below 5 GHz is very crowded as many technologies are using it so to boost up the data transmission rate 5G wireless network uses mmWave technology which instead of increasing spectrum utilization, increases the spectrum bandwidth [ 58 ]. To maximize the signal bandwidth in wireless communication the carrier frequency should also be increased by 5% because the signal bandwidth is directly proportional to carrier frequencies. The frequency band between 28 GHz to 60 GHz is very useful for 5G wireless communication as 28 GHz frequency band offers up to 1 GHz spectrum bandwidth and 60 GHz frequency band offers 2 GHz spectrum bandwidth. 4G LTE provides 2 GHz carrier frequency which offers only 100 MHz spectrum bandwidth. However, the use of mmWave increases the spectrum bandwidth 10 times, which leads to better transmission speeds [ 59 , 60 ].

Highlights of 5G mmWave are as follows:

Pictorial representation of millimeter wave.

• The 5G mmWave offer three advantages: (1) MmWave is very less used new Band, (2) MmWave signals carry more data than lower frequency wave, and (3) MmWave can be incorporated with MIMO antenna with the potential to offer a higher magnitude capacity compared to current communication systems.

In [ 11 ], the authors presented the survey of mmWave communications for 5G. The advantage of mmWave communications is adaptability, i.e., it supports the architectures and protocols up-gradation, which consists of integrated circuits, systems, etc. The authors over-viewed the present solutions and examined them concerning effectiveness, performance, and complexity. They also discussed the open research issues of mmWave communications in 5G concerning the software-defined network (SDN) architecture, network state information, efficient regulation techniques, and the heterogeneous system.

In [ 61 ], the authors present the recent work done by investigators in 5G; they discussed the design issues and demands of mmWave 5G antennas for cellular handsets. After that, they designed a small size and low-profile 60 GHz array of antenna units that contain 3D planer mesh-grid antenna elements. For the future prospect, a framework is designed in which antenna components are used to operate cellular handsets on mmWave 5G smartphones. In addition, they cross-checked the mesh-grid array of antennas with the polarized beam for upcoming hardware challenges.

In [ 12 ], the authors considered the suitability of the mmWave band for 5G cellular systems. They suggested a resource allocation system for concurrent D2D communications in mmWave 5G cellular systems, and it improves network efficiency and maintains network connectivity. This research article can serve as guidance for simulating D2D communications in mmWave 5G cellular systems. Massive mmWave BS may be set up to obtain a high delivery rate and aggregate efficiency. Therefore, many wireless users can hand off frequently between the mmWave base terminals, and it emerges the demand to search the neighbor having better network connectivity.

In [ 62 ], the authors provided a brief description of the cellular spectrum which ranges from 1 GHz to 3 GHz and is very crowed. In addition, they presented various noteworthy factors to set up mmWave communications in 5G, namely, channel characteristics regarding mmWave signal attenuation due to free space propagation, atmospheric gaseous, and rain. In addition, hybrid beamforming architecture in the mmWave technique is analyzed. They also suggested methods for the blockage effect in mmWave communications due to penetration damage. Finally, the authors have studied designing the mmWave transmission with small beams in nonorthogonal device-to-device communication.

This section covered various works done on 5G mmWave technology. The Table 7 shows how different author’s worked on the improvement of various parameters i.e., transmission rate, coverage, and cost, with 5G mmWave technology.

Summary of existing mmWave-based approaches in 5G technology.

4.4. 5G IoT Based Approaches

The 5G mobile network plays a big role in developing the Internet of Things (IoT). IoT will connect lots of things with the internet like appliances, sensors, devices, objects, and applications. These applications will collect lots of data from different devices and sensors. 5G will provide very high speed internet connectivity for data collection, transmission, control, and processing. 5G is a flexible network with unused spectrum availability and it offers very low cost deployment that is why it is the most efficient technology for IoT [ 63 ]. In many areas, 5G provides benefits to IoT, and below are some examples:

Smart homes: smart home appliances and products are in demand these days. The 5G network makes smart homes more real as it offers high speed connectivity and monitoring of smart appliances. Smart home appliances are easily accessed and configured from remote locations using the 5G network, as it offers very high speed low latency communication.

Smart cities: 5G wireless network also helps in developing smart cities applications such as automatic traffic management, weather update, local area broadcasting, energy saving, efficient power supply, smart lighting system, water resource management, crowd management, emergency control, etc.

Industrial IoT: 5G wireless technology will provide lots of features for future industries such as safety, process tracking, smart packing, shipping, energy efficiency, automation of equipment, predictive maintenance and logistics. 5G smart sensor technology also offers smarter, safer, cost effective, and energy-saving industrial operation for industrial IoT.

Smart Farming: 5G technology will play a crucial role for agriculture and smart farming. 5G sensors and GPS technology will help farmers to track live attacks on crops and manage them quickly. These smart sensors can also be used for irrigation control, pest control, insect control, and electricity control.

Autonomous Driving: 5G wireless network offers very low latency high speed communication which is very significant for autonomous driving. It means self-driving cars will come to real life soon with 5G wireless networks. Using 5G autonomous cars can easily communicate with smart traffic signs, objects and other vehicles running on the road. 5G’s low latency feature makes self-driving more real as every millisecond is important for autonomous vehicles, decision taking is performed in microseconds to avoid accidents [ 64 ].

Highlights of 5G IoT are as follows:

Pictorial representation of IoT with 5G.

• 5G with IoT is a new feature of next-generation mobile communication, which provides a high-speed internet connection between moderated devices. 5G IoT also offers smart homes, smart devices, sensors, smart transportation systems, smart industries, etc., for end-users to make them smarter.
• IoT deals with moderate devices which connect through the internet. The approach of the IoT has made the consideration of the research associated with the outcome of providing wearable, smart-phones, sensors, smart transportation systems, smart devices, washing machines, tablets, etc., and these diverse systems are associated to a common interface with the intelligence to connect.
• Significant IoT applications include private healthcare systems, traffic management, industrial management, and tactile internet, etc.

Plenty of approaches is devised to address the issues of IoT [ 14 , 65 , 66 ].

In [ 65 ], the paper focuses on 5G mobile systems due to the emerging trends and developing technologies, which results in the exponential traffic growth in IoT. The author surveyed the challenges and demands during deployment of the massive IoT applications with the main focus on mobile networking. The author reviewed the features of standard IoT infrastructure, along with the cellular-based, low-power wide-area technologies (LPWA) such as eMTC, extended coverage (EC)-GSM-IoT, as well as noncellular, low-power wide-area (LPWA) technologies such as SigFox, LoRa etc.

In [ 14 ], the authors presented how 5G technology copes with the various issues of IoT today. It provides a brief review of existing and forming 5G architectures. The survey indicates the role of 5G in the foundation of the IoT ecosystem. IoT and 5G can easily combine with improved wireless technologies to set up the same ecosystem that can fulfill the current requirement for IoT devices. 5G can alter nature and will help to expand the development of IoT devices. As the process of 5G unfolds, global associations will find essentials for setting up a cross-industry engagement in determining and enlarging the 5G system.

In [ 66 ], the author introduced an IoT authentication scheme in a 5G network, with more excellent reliability and dynamic. The scheme proposed a privacy-protected procedure for selecting slices; it provided an additional fog node for proper data transmission and service types of the subscribers, along with service-oriented authentication and key understanding to maintain the secrecy, precision of users, and confidentiality of service factors. Users anonymously identify the IoT servers and develop a vital channel for service accessibility and data cached on local fog nodes and remote IoT servers. The author performed a simulation to manifest the security and privacy preservation of the user over the network.

This section covered various works done on 5G IoT by multiple authors. Table 8 shows how different author’s worked on the improvement of numerous parameters, i.e., data rate, security requirement, and performance with 5G IoT.

Summary of IoT-based approaches in 5G technology.

4.5. Machine Learning Techniques for 5G

Various machine learning (ML) techniques were applied in 5G networks and mobile communication. It provides a solution to multiple complex problems, which requires a lot of hand-tuning. ML techniques can be broadly classified as supervised, unsupervised, and reinforcement learning. Let’s discuss each learning technique separately and where it impacts the 5G network.

Supervised Learning, where user works with labeled data; some 5G network problems can be further categorized as classification and regression problems. Some regression problems such as scheduling nodes in 5G and energy availability can be predicted using Linear Regression (LR) algorithm. To accurately predict the bandwidth and frequency allocation Statistical Logistic Regression (SLR) is applied. Some supervised classifiers are applied to predict the network demand and allocate network resources based on the connectivity performance; it signifies the topology setup and bit rates. Support Vector Machine (SVM) and NN-based approximation algorithms are used for channel learning based on observable channel state information. Deep Neural Network (DNN) is also employed to extract solutions for predicting beamforming vectors at the BS’s by taking mapping functions and uplink pilot signals into considerations.

In unsupervised Learning, where the user works with unlabeled data, various clustering techniques are applied to enhance network performance and connectivity without interruptions. K-means clustering reduces the data travel by storing data centers content into clusters. It optimizes the handover estimation based on mobility pattern and selection of relay nodes in the V2V network. Hierarchical clustering reduces network failure by detecting the intrusion in the mobile wireless network; unsupervised soft clustering helps in reducing latency by clustering fog nodes. The nonparametric Bayesian unsupervised learning technique reduces traffic in the network by actively serving the user’s requests and demands. Other unsupervised learning techniques such as Adversarial Auto Encoders (AAE) and Affinity Propagation Clustering techniques detect irregular behavior in the wireless spectrum and manage resources for ultradense small cells, respectively.

In case of an uncertain environment in the 5G wireless network, reinforcement learning (RL) techniques are employed to solve some problems. Actor-critic reinforcement learning is used for user scheduling and resource allocation in the network. Markov decision process (MDP) and Partially Observable MDP (POMDP) is used for Quality of Experience (QoE)-based handover decision-making for Hetnets. Controls packet call admission in HetNets and channel access process for secondary users in a Cognitive Radio Network (CRN). Deep RL is applied to decide the communication channel and mobility and speeds up the secondary user’s learning rate using an antijamming strategy. Deep RL is employed in various 5G network application parameters such as resource allocation and security [ 67 ]. Table 9 shows the state-of-the-art ML-based solution for 5G network.

The state-of-the-art ML-based solution for 5G network.

Highlights of machine learning techniques for 5G are as follows:

Pictorial representation of machine learning (ML) in 5G.

• In ML, a model will be defined which fulfills the desired requirements through which desired results are obtained. In the later stage, it examines accuracy from obtained results.
• ML plays a vital role in 5G network analysis for threat detection, network load prediction, final arrangement, and network formation. Searching for a better balance between power, length of antennas, area, and network thickness crossed with the spontaneous use of services in the universe of individual users and types of devices.

In [ 79 ], author’s firstly describes the demands for the traditional authentication procedures and benefits of intelligent authentication. The intelligent authentication method was established to improve security practice in 5G-and-beyond wireless communication systems. Thereafter, the machine learning paradigms for intelligent authentication were organized into parametric and non-parametric research methods, as well as supervised, unsupervised, and reinforcement learning approaches. As a outcome, machine learning techniques provide a new paradigm into authentication under diverse network conditions and unstable dynamics. In addition, prompt intelligence to the security management to obtain cost-effective, better reliable, model-free, continuous, and situation-aware authentication.

In [ 68 ], the authors proposed a machine learning-based model to predict the traffic load at a particular location. They used a mobile network traffic dataset to train a model that can calculate the total number of user requests at a time. To launch access and mobility management function (AMF) instances according to the requirement as there were no predictions of user request the performance automatically degrade as AMF does not handle these requests at a time. Earlier threshold-based techniques were used to predict the traffic load, but that approach took too much time; therefore, the authors proposed RNN algorithm-based ML to predict the traffic load, which gives efficient results.

In [ 15 ], authors discussed the issue of network slice admission, resource allocation among subscribers, and how to maximize the profit of infrastructure providers. The author proposed a network slice admission control algorithm based on SMDP (decision-making process) that guarantees the subscribers’ best acceptance policies and satisfiability (tenants). They also suggested novel N3AC, a neural network-based algorithm that optimizes performance under various configurations, significantly outperforms practical and straightforward approaches.

This section includes various works done on 5G ML by different authors. Table 10 shows the state-of-the-art work on the improvement of various parameters such as energy efficiency, Quality of Services (QoS), and latency with 5G ML.

The state-of-the-art ML-based approaches in 5G technology.

4.6. Optimization Techniques for 5G

Optimization techniques may be applied to capture NP-Complete or NP-Hard problems in 5G technology. This section briefly describes various research works suggested for 5G technology based on optimization techniques.

In [ 80 ], Massive MIMO technology is used in 5G mobile network to make it more flexible and scalable. The MIMO implementation in 5G needs a significant number of radio frequencies is required in the RF circuit that increases the cost and energy consumption of the 5G network. This paper provides a solution that increases the cost efficiency and energy efficiency with many radio frequency chains for a 5G wireless communication network. They give an optimized energy efficient technique for MIMO antenna and mmWave technologies based 5G mobile communication network. The proposed Energy Efficient Hybrid Precoding (EEHP) algorithm to increase the energy efficiency for the 5G wireless network. This algorithm minimizes the cost of an RF circuit with a large number of RF chains.

In [ 16 ], authors have discussed the growing demand for energy efficiency in the next-generation networks. In the last decade, they have figured out the things in wireless transmissions, which proved a change towards pursuing green communication for the next generation system. The importance of adopting the correct EE metric was also reviewed. Further, they worked through the different approaches that can be applied in the future for increasing the network’s energy and posed a summary of the work that was completed previously to enhance the energy productivity of the network using these capabilities. A system design for EE development using relay selection was also characterized, along with an observation of distinct algorithms applied for EE in relay-based ecosystems.

In [ 81 ], authors presented how AI-based approach is used to the setup of Self Organizing Network (SON) functionalities for radio access network (RAN) design and optimization. They used a machine learning approach to predict the results for 5G SON functionalities. Firstly, the input was taken from various sources; then, prediction and clustering-based machine learning models were applied to produce the results. Multiple AI-based devices were used to extract the knowledge analysis to execute SON functionalities smoothly. Based on results, they tested how self-optimization, self-testing, and self-designing are done for SON. The author also describes how the proposed mechanism classifies in different orders.

In [ 82 ], investigators examined the working of OFDM in various channel environments. They also figured out the changes in frame duration of the 5G TDD frame design. Subcarrier spacing is beneficial to obtain a small frame length with control overhead. They provided various techniques to reduce the growing guard period (GP) and cyclic prefix (CP) like complete utilization of multiple subcarrier spacing, management and data parts of frame at receiver end, various uses of timing advance (TA) or total control of flexible CP size.

This section includes various works that were done on 5G optimization by different authors. Table 11 shows how other authors worked on the improvement of multiple parameters such as energy efficiency, power optimization, and latency with 5G optimization.

Summary of Optimization Based Approaches in 5G Technology.

5. Description of Novel 5G Features over 4G

This section presents descriptions of various novel features of 5G, namely, the concept of small cell, beamforming, and MEC.

5.1. Small Cell

Small cells are low-powered cellular radio access nodes which work in the range of 10 meters to a few kilometers. Small cells play a very important role in implementation of the 5G wireless network. Small cells are low power base stations which cover small areas. Small cells are quite similar with all the previous cells used in various wireless networks. However, these cells have some advantages like they can work with low power and they are also capable of working with high data rates. Small cells help in rollout of 5G network with ultra high speed and low latency communication. Small cells in the 5G network use some new technologies like MIMO, beamforming, and mmWave for high speed data transmission. The design of small cells hardware is very simple so its implementation is quite easier and faster. There are three types of small cell tower available in the market. Femtocells, picocells, and microcells [ 83 ]. As shown in the Table 12 .

Types of Small cells.

MmWave is a very high band spectrum between 30 to 300 GHz. As it is a significantly less used spectrum, it provides very high-speed wireless communication. MmWave offers ultra-wide bandwidth for next-generation mobile networks. MmWave has lots of advantages, but it has some disadvantages, too, such as mmWave signals are very high-frequency signals, so they have more collision with obstacles in the air which cause the signals loses energy quickly. Buildings and trees also block MmWave signals, so these signals cover a shorter distance. To resolve these issues, multiple small cell stations are installed to cover the gap between end-user and base station [ 18 ]. Small cell covers a very shorter range, so the installation of a small cell depends on the population of a particular area. Generally, in a populated place, the distance between each small cell varies from 10 to 90 meters. In the survey [ 20 ], various authors implemented small cells with massive MIMO simultaneously. They also reviewed multiple technologies used in 5G like beamforming, small cell, massive MIMO, NOMA, device to device (D2D) communication. Various problems like interference management, spectral efficiency, resource management, energy efficiency, and backhauling are discussed. The author also gave a detailed presentation of all the issues occurring while implementing small cells with various 5G technologies. As shown in the Figure 7 , mmWave has a higher range, so it can be easily blocked by the obstacles as shown in Figure 7 a. This is one of the key concerns of millimeter-wave signal transmission. To solve this issue, the small cell can be placed at a short distance to transmit the signals easily, as shown in Figure 7 b.

Pictorial representation of communication with and without small cells.

5.2. Beamforming

Beamforming is a key technology of wireless networks which transmits the signals in a directional manner. 5G beamforming making a strong wireless connection toward a receiving end. In conventional systems when small cells are not using beamforming, moving signals to particular areas is quite difficult. Beamforming counter this issue using beamforming small cells are able to transmit the signals in particular direction towards a device like mobile phone, laptops, autonomous vehicle and IoT devices. Beamforming is improving the efficiency and saves the energy of the 5G network. Beamforming is broadly divided into three categories: Digital beamforming, analog beamforming and hybrid beamforming. Digital beamforming: multiuser MIMO is equal to digital beamforming which is mainly used in LTE Advanced Pro and in 5G NR. In digital beamforming the same frequency or time resources can be used to transmit the data to multiple users at the same time which improves the cell capacity of wireless networks. Analog Beamforming: In mmWave frequency range 5G NR analog beamforming is a very important approach which improves the coverage. In digital beamforming there are chances of high pathloss in mmWave as only one beam per set of antenna is formed. While the analog beamforming saves high pathloss in mmWave. Hybrid beamforming: hybrid beamforming is a combination of both analog beamforming and digital beamforming. In the implementation of MmWave in 5G network hybrid beamforming will be used [ 84 ].

Wireless signals in the 4G network are spreading in large areas, and nature is not Omnidirectional. Thus, energy depletes rapidly, and users who are accessing these signals also face interference problems. The beamforming technique is used in the 5G network to resolve this issue. In beamforming signals are directional. They move like a laser beam from the base station to the user, so signals seem to be traveling in an invisible cable. Beamforming helps achieve a faster data rate; as the signals are directional, it leads to less energy consumption and less interference. In [ 21 ], investigators evolve some techniques which reduce interference and increase system efficiency of the 5G mobile network. In this survey article, the authors covered various challenges faced while designing an optimized beamforming algorithm. Mainly focused on different design parameters such as performance evaluation and power consumption. In addition, they also described various issues related to beamforming like CSI, computation complexity, and antenna correlation. They also covered various research to cover how beamforming helps implement MIMO in next-generation mobile networks [ 85 ]. Figure 8 shows the pictorial representation of communication with and without using beamforming.

Pictorial Representation of communication with and without using beamforming.

5.3. Mobile Edge Computing

Mobile Edge Computing (MEC) [ 24 ]: MEC is an extended version of cloud computing that brings cloud resources closer to the end-user. When we talk about computing, the very first thing that comes to our mind is cloud computing. Cloud computing is a very famous technology that offers many services to end-user. Still, cloud computing has many drawbacks. The services available in the cloud are too far from end-users that create latency, and cloud user needs to download the complete application before use, which also increases the burden to the device [ 86 ]. MEC creates an edge between the end-user and cloud server, bringing cloud computing closer to the end-user. Now, all the services, namely, video conferencing, virtual software, etc., are offered by this edge that improves cloud computing performance. Another essential feature of MEC is that the application is split into two parts, which, first one is available at cloud server, and the second is at the user’s device. Therefore, the user need not download the complete application on his device that increases the performance of the end user’s device. Furthermore, MEC provides cloud services at very low latency and less bandwidth. In [ 23 , 87 ], the author’s investigation proved that successful deployment of MEC in 5G network increases the overall performance of 5G architecture. Graphical differentiation between cloud computing and mobile edge computing is presented in Figure 9 .

Pictorial representation of cloud computing vs. mobile edge computing.

6. 5G Security

Security is the key feature in the telecommunication network industry, which is necessary at various layers, to handle 5G network security in applications such as IoT, Digital forensics, IDS and many more [ 88 , 89 ]. The authors [ 90 ], discussed the background of 5G and its security concerns, challenges and future directions. The author also introduced the blockchain technology that can be incorporated with the IoT to overcome the challenges in IoT. The paper aims to create a security framework which can be incorporated with the LTE advanced network, and effective in terms of cost, deployment and QoS. In [ 91 ], author surveyed various form of attacks, the security challenges, security solutions with respect to the affected technology such as SDN, Network function virtualization (NFV), Mobile Clouds and MEC, and security standardizations of 5G, i.e., 3GPP, 5GPPP, Internet Engineering Task Force (IETF), Next Generation Mobile Networks (NGMN), European Telecommunications Standards Institute (ETSI). In [ 92 ], author elaborated various technological aspects, security issues and their existing solutions and also mentioned the new emerging technological paradigms for 5G security such as blockchain, quantum cryptography, AI, SDN, CPS, MEC, D2D. The author aims to create new security frameworks for 5G for further use of this technology in development of smart cities, transportation and healthcare. In [ 93 ], author analyzed the threats and dark threat, security aspects concerned with SDN and NFV, also their Commercial & Industrial Security Corporation (CISCO) 5G vision and new security innovations with respect to the new evolving architectures of 5G [ 94 ].

AuthenticationThe identification of the user in any network is made with the help of authentication. The different mobile network generations from 1G to 5G have used multiple techniques for user authentication. 5G utilizes the 5G Authentication and Key Agreement (AKA) authentication method, which shares a cryptographic key between user equipment (UE) and its home network and establishes a mutual authentication process between the both [ 95 ].

Access Control To restrict the accessibility in the network, 5G supports access control mechanisms to provide a secure and safe environment to the users and is controlled by network providers. 5G uses simple public key infrastructure (PKI) certificates for authenticating access in the 5G network. PKI put forward a secure and dynamic environment for the 5G network. The simple PKI technique provides flexibility to the 5G network; it can scale up and scale down as per the user traffic in the network [ 96 , 97 ].

Communication Security 5G deals to provide high data bandwidth, low latency, and better signal coverage. Therefore secure communication is the key concern in the 5G network. UE, mobile operators, core network, and access networks are the main focal point for the attackers in 5G communication. Some of the common attacks in communication at various segments are Botnet, message insertion, micro-cell, distributed denial of service (DDoS), and transport layer security (TLS)/secure sockets layer (SSL) attacks [ 98 , 99 ].

Encryption The confidentiality of the user and the network is done using encryption techniques. As 5G offers multiple services, end-to-end (E2E) encryption is the most suitable technique applied over various segments in the 5G network. Encryption forbids unauthorized access to the network and maintains the data privacy of the user. To encrypt the radio traffic at Packet Data Convergence Protocol (PDCP) layer, three 128-bits keys are applied at the user plane, nonaccess stratum (NAS), and access stratum (AS) [ 100 ].

7. Summary of 5G Technology Based on Above-Stated Challenges

In this section, various issues addressed by investigators in 5G technologies are presented in Table 13 . In addition, different parameters are considered, such as throughput, latency, energy efficiency, data rate, spectral efficiency, fairness & computing capacity, transmission rate, coverage, cost, security requirement, performance, QoS, power optimization, etc., indexed from R1 to R14.

Summary of 5G Technology above stated challenges (R1:Throughput, R2:Latency, R3:Energy Efficiency, R4:Data Rate, R5:Spectral efficiency, R6:Fairness & Computing Capacity, R7:Transmission Rate, R8:Coverage, R9:Cost, R10:Security requirement, R11:Performance, R12:Quality of Services (QoS), R13:Power Optimization).

8. Conclusions

This survey article illustrates the emergence of 5G, its evolution from 1G to 5G mobile network, applications, different research groups, their work, and the key features of 5G. It is not just a mobile broadband network, different from all the previous mobile network generations; it offers services like IoT, V2X, and Industry 4.0. This paper covers a detailed survey from multiple authors on different technologies in 5G, such as massive MIMO, Non-Orthogonal Multiple Access (NOMA), millimeter wave, small cell, MEC (Mobile Edge Computing), beamforming, optimization, and machine learning in 5G. After each section, a tabular comparison covers all the state-of-the-research held in these technologies. This survey also shows the importance of these newly added technologies and building a flexible, scalable, and reliable 5G network.

9. Future Findings

This article covers a detailed survey on the 5G mobile network and its features. These features make 5G more reliable, scalable, efficient at affordable rates. As discussed in the above sections, numerous technical challenges originate while implementing those features or providing services over a 5G mobile network. So, for future research directions, the research community can overcome these challenges while implementing these technologies (MIMO, NOMA, small cell, mmWave, beam-forming, MEC) over a 5G network. 5G communication will bring new improvements over the existing systems. Still, the current solutions cannot fulfill the autonomous system and future intelligence engineering requirements after a decade. There is no matter of discussion that 5G will provide better QoS and new features than 4G. But there is always room for improvement as the considerable growth of centralized data and autonomous industry 5G wireless networks will not be capable of fulfilling their demands in the future. So, we need to move on new wireless network technology that is named 6G. 6G wireless network will bring new heights in mobile generations, as it includes (i) massive human-to-machine communication, (ii) ubiquitous connectivity between the local device and cloud server, (iii) creation of data fusion technology for various mixed reality experiences and multiverps maps. (iv) Focus on sensing and actuation to control the network of the entire world. The 6G mobile network will offer new services with some other technologies; these services are 3D mapping, reality devices, smart homes, smart wearable, autonomous vehicles, artificial intelligence, and sense. It is expected that 6G will provide ultra-long-range communication with a very low latency of 1 ms. The per-user bit rate in a 6G wireless network will be approximately 1 Tbps, and it will also provide wireless communication, which is 1000 times faster than 5G networks.

Acknowledgments

Author contributions.

Conceptualization: R.D., I.Y., G.C., P.L. data gathering: R.D., G.C., P.L, I.Y. funding acquisition: I.Y. investigation: I.Y., G.C., G.P. methodology: R.D., I.Y., G.C., P.L., G.P., survey: I.Y., G.C., P.L, G.P., R.D. supervision: G.C., I.Y., G.P. validation: I.Y., G.P. visualization: R.D., I.Y., G.C., P.L. writing, original draft: R.D., I.Y., G.C., P.L., G.P. writing, review, and editing: I.Y., G.C., G.P. All authors have read and agreed to the published version of the manuscript.

This paper was supported by Soonchunhyang University.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Wireless Communication Thesis Topics

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Communication Thesis Topics

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Let’s view some of the important wireless communication areas and recent topics that are as follow,

Major research areas, control systems:.

• Traffic light controller
• Controlling mobile Robots
• Speed control also for induction motors
• Synchronous Dynamic Random Access Memory [SDRAM] controller
• Fuzzy logic based DC motor control system
• Speed control system also for high speed train
• Armature controlled DC motor
• Schneider electric’s Esmi access control system
• BMW’s voice recognition system
• New drone control system
• Aperio and Axis bring wireless access control
• Acuity’s xCella wireless control system
• Movcan 3-axis wireless lens control system
• SMARTair Pro
• Photovoltaic system control
• Turnkey sensor system
• RAE systems
• Automated Solar [Microcontroller [89S51, 8051, 89C51], Analog to digital converters 0808]
• Air management and also environment
• Aerodynamics
• Hydraulics system
• Propulsion and fuel
• Airborne wireless network
• Radar remote sensing
• Astrodynamics
• Electro technology
• Aeroelasticity, Avionics and also Aeroacoustics
• Infrared sensors market Aerospace
• Nanoscale wireless communication
• Avionics-Electronics cooling
• UTC Aerospace systems

Power Line Communications:

• Hybrid electric vehicle
• White electric vehicle
• Monitoring distributed transformers
• Data concentrators
• Electricity meters
• Smart metering
• Energy gateways
• In Home display units
• Security systems / Keyless entry
• Sensor control and also Data Acquisition
• Industrial Automation
• Building Automation
• Hybrid optoelectronic sensor [Monitoring overhead also in transmission line]
• Heating Ventilation and Air Conditioning(HVAC)

Power Generation [Smart Grid]:

• Thermal power plant
• Nuclear power plant
• Wind power plant
• Hydroelectric power station
• Solar, Wind and also Marine
• Osmosis and also Biomass
• Steel mills, blast furnace exhaust gas
• Geothermal power
• Hydraulic power generation
• Photovoltaic cell
• Steam turbine and Gas turbine
• Fossil- fuel power stations

VLSI [Very Large Scale Integrated Circuits]

• STA simplification and DFT
• Design rule optimization
• DRC automation
• High performance transistor designing
• EDA algorithm improvement
• Data processing neuromorphic chip
• FPGA-module1 also for VLSI design
• Integrated photonics
• Mitigate chip package interaction
• RoBA multiplier
• Reducing microfluidic (VLSI)
• Equipment design and also material design
• 3D power scaling
• Photonic transmitters beyond LED
• Application development in the areas [Like IOT, IOE, Big data, Cloud computing, Machine learning, Artificial intelligence, and also Deep learning]

Embedded Systems:

• Micro controller also for Serial port Oscilloscope
• Servo motor control
• Temperature monitoring system also using PIC18F4550
• Interfacing circuit for controlling stepper motor and also LED
• Ultrasound sensor interfacing also with Arduino
• Multiple parameter monitoring systems
• Navigation and also guidance systems
• Circuit debuggers or emulators
• Feedback and data collection systems
• Heart beat and also blood pressure monitor
• Electronic fuel injection systems
• Serial communication interfaces
• DiscreteIO (aka general purpose input/output (GPIO))
• Multimedia cards (Comfact flash, also SD cards, etc)

Current Real Time Applications

• Smart oil field management
• Locating a mobile station
• Leader follower base mobile molecular communication
• Adaptive channel diversity
• Reducing sounding overhead
• Charging time control (Wireless power transfer system)
• Distributed decision making and also control
• Sharing frequencies (OFDM also based)
• Reconfigurable farrow structure (FRM Filters)
• Stochastic communications delays (Stability and also frequency response)
• Information handling system (Secure RF)
• Passive frequency multiplier designs (K-band)
• Energy efficiency enhancement (cryptographic techniques)
• Novel power wireless broadband system

Recent Research Topics

• Wireless communications for advances on exploiting polarization
• Wireless-communications with new applications also for MEMS modular robots
• Secure wireless communication also for application of full duplex guarantees
• Mobile control and safety applications in industrial environments also for reliable wireless communication and positioning
• Arbitrary patch antenna design also for wireless communications using application of opposition based learning concepts
• Application in wireless communication also using a simple multiband parch antenna
• Applications to wireless communications performance analysis also for generalized MGF of Beckmann fading
• Low cost wireless communications applications also using grapheme flakes printed wideband elliptical dipole antenna
• Applications to wireless communications also for calculation of the incomplete MGF
• Future wireless standards using high throughput and also area efficient rotated and cyclic Q delayed constellations demapper
• Circularly polarized antenna also for compact omnidirectional
• Ultra wideband communication applications also for compact circularly polarized Archimedean spiral antenna
• LC-VCOs also using high performance switchable multiband inductor structure
• Generalization of the Doherty theory also for design of linear and efficient power amplifiers
• Wireless communications also with compressed sensing usefulness
• Generating electromagnetic vortex wave also using design of reflective phase shifting
• Wireless communications also using quad-band perforated rectangular dielectric resonator antenna
• Cooperative automated vehicle applications also using utilizing model based communication
• Synthesis of non-uniformly spaced linear antenna also for PEEC based multi objective

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MTech Thesis

With wireless communication becoming an integral part of human life, the improvement of the performance of any wireless network has become a topic of keen interest of the researchers. The path of propagation being wireless, the performance of the network is affected consequently by the topology and the environmental conditions of the area where the network is deployed. Hence, a study of the performance of the widely researched WiMAX network is performed under varying terrain and environmental conditions for various propagation models in this thesis. Also with 4G mobile networks coming up, the performance of the integrated WLAN-WiMAX network is also evaluated and compared with the existing WLAN and WiMAX technologies. Voice over IP is considered as the application as it is expected to be a low cost and thereby, a popular communication system in the next generation communication networks. The digitization of the analog voice signal before transmission is done by the voice codecs. Hence, a study of the performance of the networks and VoIP is also conducted here with different codecs for the three technologies. It is observed that the network performance is best for free space path loss model as it considers the communication path to be free from obstacles. Also the WLAN-WiMAX integrated network is observed to perform best among the three technologies with respect to network capacity and network performance.

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With wireless communication becoming an integral part of human life, the improvement of the performance of any wireless network has become a topic of keen interest of the researchers. The path of propagation being wireless, the performance of the network is affected consequently by the topology and the environmental conditions of the area where the network is deployed.

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The improvement of the performance of any wireless network has become a topic of keen interest of the researchers. The path of propagation being wireless, the performance of the network is affected consequently by the topology and the environmental conditions of the area where the network is deployed. Hence, a study of the performance of the widely researched WiMAX network is performed under varying terrain and environmental conditions for various propagation models in this thesis. Also with 4G mobile networks coming up, the performance of the integrated WLAN-WiMAX network is also evaluated and compared with the existing WLAN and WiMAX technologies. Voice over IP is considered as the application as it is expected to be a low cost and thereby, a popular communication system in the next generation communication networks. The digitization of the analog voice signal before transmission is done by the voice codecs. Hence, a study of the performance of the networks and VoIP is also conducted here with different codecs for the three technologies. It is observed that the network performance is best for free space path loss model as it considers the communication path to be free from obstacles. Also the WLAN-WiMAX integrated network is observed to perform best among the three technologies with respect to network capacity and network performance.

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WiMAX (Worldwide Interoperability for Microwave Access) is broadband wireless technology for providing last mile solutions for supporting higher bandwidth and multiple service classes with various quality of service requirement. In this paper we concentrated on the applications of the WiMAX in our daily life. The paper constructed three scenarios by using OPNET modeler 14.5. First: WiMAX connection, to examine its efficiency to connection from base station to the WiMAX work station, it is found that the less number of work stations and the less distances gave a better performance for WiMAX. Second: WLAN-WiMAX to test the single FTP performance, It is observed the FTP drop dramatically when the Distance Between work station and WLAN router increases. Third: Effect of base frequency for the WiMAX network, it found that the lower base frequency (2.4GHz) The higher performance of WIMAX network.

International Journal of Engineering Research and Technology (IJERT)

IJERT Journal

https://www.ijert.org/performance-evaluation-of-wimax-network https://www.ijert.org/research/performance-evaluation-of-wimax-network-IJERTV1IS8494.pdf WiMAX networks, built on all-IP network architecture for plug and play network deployments, can support a mix of different usage and service models. While some consider mobile WiMAX as a candidate for the fourth generation of mobile networks, others view it as the first generation of mobile Internet technologies emerging from a wider ecosystem targeting to extend the success of WiFi over wide area networks supporting mobility. WiMAX is one of the important broadband wireless technologies .Being an emerging technology, WiMAX supports multimedia applications such as voice over IP (VoIP), voice conference and online gaming The use of different modulation schemes like QPSK, QAM gives better flexibility for WiMAX network. In this paper, we analysed and simulated the different modulation schemes for WiMAX.

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A new kind of chip for wireless communication

January 5, 2024

Article by Wayne Gillam , Photos by Ryan Hoover | UW ECE News

A UW ECE graduate student research team, advised and led by Professor Chris Rudell, has designed an innovative computer chip that can send and receive large amounts of data at high speeds while minimizing signal distortion and conserving the limited spectrum available for wireless communication. Shown above: A close-up view of the chip designed by the team.

Imagine the roar of a crowd while sitting in a football stadium during a touchdown. At that moment of peak excitement, envision trying to carry on a conversation with a friend on the opposite side of the stadium. You’re using a megaphone to be heard across the distance and the noise, but your friend is whispering. And on top of that, you’re both talking at the same time. Sounds impossible? It probably is. However, in the near future, wireless communication technologies, such as Bluetooth, WiFi, and cellular radios, will face a challenge very similar to this hypothetical scenario, but in the open-air, electromagnetic spectrum.

With each passing year, the number of wireless devices owned by consumers is steadily increasing , creating more congestion over the airwaves — much like adding to the number of noisy fans in our imaginary football stadium. This unfortunate reality can lead to problems with signal interference between wireless devices, so engineers are motivated to look for ways of making better use of a finite amount of shared spectrum allocated for wireless communication. Currently, one avenue being pursued seeks to explore new ways of transmitting and receiving signals using the same carrier frequency to both talk (transmit) and listen (receive). This would cut in half the amount of shared spectrum that is occupied by one device. But this method of communication brings up a thorny challenge — coping with signal noise and distortion caused by transmitting and receiving at the same time, especially for mobile technologies attempting to communicate over long distances.

“The impact of this chip will extend not only to all wireless devices, but also to many other devices that serve as an interface between the real and digital worlds. At the end of the day, its going to be very broad in its application.” — UW ECE Professor Chris Rudell

The challenge grows as a mobile device approaches the edge of its signal range. A device transmitting and receiving at the same time and on the same carrier frequency must be able to “listen” carefully to both detect and receive a faint signal sent from a long distance, while simultaneously transmitting a powerful signal back to the other user to establish two-way communication. This is akin to shouting as loudly as possible at someone while they are whispering from the far end of a long hallway, or from across a football stadium, like in the scenario described above.

UW ECE Professor Chris Rudell

Now, a UW ECE graduate student research team, advised and led by Professor Chris Rudell , has found a way to help address this daunting challenge with a hardware solution fabricated on a single computer chip. Over the past three years, the team has designed this chip for wireless communication, engineering it to send and receive large amounts of data at high speeds on the same carrier frequency while also minimizing signal distortion. They described their work in a recent paper published in the IEEE Journal of Solid-State Circuits.

“The type of chip we developed is very important, and that’s because signal interference is only becoming worse as time goes on. As the number of electronic and wireless devices increases, the interference patterns that exist out there are becoming increasingly problematic,” Rudell said. “They really limit the bandwidth that can be achieved: the data rate, the range, the reliability of the wireless connection and the performance demanded by the end user.”

Rudell’s research team included graduate students Xichen Li, Yi-Hsiang Huang and Fucheng Yin* (BSEE ‘18, MSEE ‘21). The promise of their work was recognized early on, with Li and Huang both receiving a Qualcomm Innovation Fellowship for the initial stages of the research. The team also received support from the Washington Research Foundation and UW CoMotion . And because this is innovative work important to the semiconductor industry, the group received funding and support from the Semiconductor Research Corporation (SRC)/ Intel , as well as Qualcomm , Boeing , the Center for Design of Analog-Digital Integrated Circuits (CDADIC), and Marvell Technology Group .

In September 2023, the research team received an Institute of Electrical and Electronics Engineers (IEEE) European Solid-State Circuits Conference (ESSCIRC) Best Student Paper Award in Lisbon, Portugal, which recognized the merit of their work on an international stage.

“The ESSCIRC Award validates the quality of our research and demonstrates acceptance by our peers worldwide,” Rudell said. “It shows that the community perceives our work as cutting edge, and I personally find that very rewarding.”

Improved data capacity, speed and signal clarity

UW ECE graduate students Yi-Hsiang Huang (left) and Xichen Li (right) working in the lab of Professor Chris Rudell

The chip the team designed has the potential to improve data capacity, speed and signal clarity for almost every type of wireless communication, both short and long range. The advance is especially important for long-promised technologies, such as self-driving cars, which can be thought of as mobile devices that need to send, receive, and process large amounts of wireless data across long distances, and at lightning speed. Other wireless communication devices and applications that could benefit from this new chip include smartphones; laptops; 5G and 6G cellular technologies; medical imaging techniques, such as magnetic resonance imaging (MRI) and positron emission tomography (PET); and new and emerging types of data-intensive electronic interfaces for implantable biomedical applications, such as brain-computer interfaces.

This chip is an “analog, mixed-signal, and radio-frequency (RF) chip,” which means that it acts as an interface between analog and digital signals, or put another way, the chip acts as a bridge between radio signals carried over the airwaves and the digital electronics inside a wireless device. The chip also uses digital computation to reduce signal distortion. That is not unique in itself; however, this chip can improve signal clarity while handling much more data at faster speeds than most of its counterparts in other research labs across the globe.

“This is one of the widest bandwidth, self-interference cancellation radios on a chip of its type,” Rudell said. “We use digital computation to assist the analog performance and address the technical challenges from a top-down approach by exploring the overall radio system down to the circuit block level. This yields a solution that minimizes signal distortion, is more energy efficient and is highly programmable, allowing the System-on-a-Chip, or ‘SoC,’ to tune the circuits for a wide range of radio applications.”

Machine learning, mass production and commercialization

Yi-Hsiang Huang (left) and Xichen Li (right)

Looking toward the future, Rudell said that he sees machine learning playing a key role in the chip’s development. Many fast, complex calculations will be necessary to continuously tune the chip circuits for signal interference cancellation while the device moves through a vast range of physical positions and different environments. Rudell is planning to build an updated version of the chip that uses machine learning for this purpose, and he is currently in conversations with organizations that could fund and support this next step in the chip’s development.

Also being planned is mass production of the chip. Rudell is looking into commercializing this technology, and he noted the challenges involved with bringing a new chip design into a wide range of different manufacturing conditions. To be commercially viable at a large scale, a chip such as this needs to be mass produced in quantities ranging from millions to billions. In addition, each chip must work smoothly over long periods of time despite changes in environmental conditions and variations in temperature and battery supply voltage. Despite these hurdles, Rudell said he is confident that a future iteration of this chip will make its way to the marketplace within two to four years.

“There are so many different types of things that could benefit from this chip technology,” Rudell said. “You have wireless transceivers, radar, cable set top boxes, medical imaging applications, and more. The impact of this chip will extend not only to all wireless devices, but also to many other devices that serve as an interface between the real and digital worlds. At the end of the day, it’s going to be very broad in its application.”

For more information about the research described in this article, read “ A 2.4GHz Full-Duplex Transceiver with Broadband (+120MHz), Linearity-Calibrated and Long-Delayed Self-Interference Cancellation ” in the IEEE Journal of Solid-State Circuits, or contact UW ECE Professor Chris Rudell .

*In Memoriam: Fucheng Yin

UW ECE alumnus Fucheng Yin (BSEE ‘18, MSEE ‘21). Photo provided by the Future Analog Systems Technologies Lab.

UW ECE would like to express our deepest condolences to the family and friends of Fucheng Yin. Fucheng was a co-author of the IEEE Journal of Solid-State Circuits paper described in the above article and an important member of Professor Rudell’s graduate student research group.

Fucheng was born in Guangzhou, Guangdong, China, in 1989. He received his bachelor’s and master’s degrees from UW ECE in 2018 and 2021, respectively. During the summer of 2021, he held an intern position with the Radio Frequency (RF) Group at Impinj in Seattle. In January 2022, he joined Qualcomm in Santa Clara, California, as an RF Integrated Circuit (IC) Designer.

As a UW ECE graduate student, Fucheng was extremely committed to research and teaching. His research interests included the area of radio frequency and millimeter-wave integrated circuit design, electromagnetics, and device physics. He was well liked by the faculty and his fellow students.

Fucheng passed away in 2022 after a long and courageous battle with an illness. He had expressed to his family, friends, and colleagues that it was a lifelong dream of his to publish a research paper in an IEEE Journal. With the paper described in this article, he not only accomplished that goal but also was posthumously a co-recipient of the Best Student Paper Award at the 2022 IEEE European Solid-State Circuits Conference for this work related to his master’s thesis.

All of us at UW ECE, including Professor Rudell and his graduate student research group, are very grateful to Fucheng for his collegiality and many contributions to the classroom and the lab over the years. He will be greatly missed.

Be boundless

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Thesis topics in Wireless Communication

Wireless Communication is soon going to replace the traditional wired mode of communication. With the advent of wireless technology, communication has become more convenient and effective. Now there is no need to install lengthy wires to set up the network.

Wireless technology uses radio waves for communication rather than relying on wires. There are various topics in wireless communication for thesis, project and research. Following are the list of current thesis topics in wireless communication :

MIMO(Multiple Input, Multiple Output)

Wlan(wireless local area network).

• WANET(Wireless ad-hoc Network)

IoT(Internet of Things)

• ZigBee technology

MIMO is an antenna-based technology of wireless communication in which both sender and receiver uses multiple antennas to enhance the capacity of a radio link. It reduces errors and optimizes data speed by the combining the antennas at each end. It is also referred as a smart antenna technology. It is very good topic for thesis and research in wireless communication.

In traditional wireless communication methods, single antenna is used at source end as well as the destination end. Certain problems arise due to this like scattering of data signals, fading, intermittent reception, reduction in data speed and more number of errors. Due to scattering of data signals, the problem of multipath wave propagation arises. Use of multiple antennas knock-out the problem caused due to multipath wave propagation. Different form of antenna links are:

SISO – Single Input, Single Output

SIMO – Single Input, Multiple Output

MISO – Multiple Input, Single Output

MIMO – Multiple Input, Multiple Output

Functions of MIMO

The functions of MIMO can be classified into following three categories:

Precoding – It is a spatial process of multi-stream beamforming that occurs at the transmitter. In beamforming, a signal is sent from the transmitter which is amplified while it reaches the receiver. It increases the signal strength and also reduces the multipath fading effect.

Spatial Multiplexing – Spatial multiplexing is a technique to increase the channel capacity. In this technique, a signal which is of high-rate is divided into several low-rate signals keeping the frequency channel same. These signals arrive at the receiver end with different spatial signatures. Spatial Multiplexing can be combined with precoding if channel state information(CSI) is available.

Diversity Coding – This technique is used when there is no information of CSI at the transmitter. Unlike spatial multiplexing in which multiple streams are transmitted, single stream is transmitted in diversity methods and the signal is coded by the employing the technique of space-time coding. Diversity coding can be combined with spatial multiplexing if CSI is available.

Formats of MIMO

Following are the two formats of MIMO:

Spatial diversity – It refers to the diversity in transmitting and receiving and help in improving the signal to noise ratio.

Spatial multiplexing – As earlier explained, spatial multiplexing improves the channel throughput capability.

Applications of MIMO

MIMO find its applications in various areas. Following are the main applications of MIMO:

MIMO is used in mobile radio telephones in standards like 3GPPP and 3GPP2. HSPA and LTE support MIMO.

MIMO technology is also used in non-wireless communication systems like home networking standard to transmit multiple signals.

WLAN stands for Wireless Local Area Network. It is a wireless network of two or more devices and uses high-frequency radio waves for communication. This network has an access point to the internet. The communication is for limited coverage area like homes, offices, schools. WLAN is based on IEEE 802.11 standard and commonly referred as Wi-Fi. This network is for commercial use as it is easy to install and use. It is another choice for thesis in wireless communication.

There are two main components of WLAN:

Access Point

station and which requires access point are referred to as infrastructure base station.

Types of Wireless LAN

There are two types of WLAN based on their mode of operation:

Infrastructure mode

ad-hoc mode

In infrastructure mode, devices communicate through an access point while in ad-hoc mode the devices communicate directly. In infrastructure mode, base station act as the access point hub and all the nodes communicate through that hub. In ad hoc nodes use peer to peer method of communication with each other.

Applications of WLAN

There are many real-world applications of WLAN due to enhanced capabilities than wired network. Following are the application areas of Wireless LAN:

Healthcare – Through WLAN, the doctors and physicians can access patient’s data at a faster rate. WLAN can be used to communicate with other doctors in case of emergency situations. The data of a patient’s health at a distant location can also be accessed through this wireless network.

Everyday business use – Wireless LAN is used in schools, colleges, and offices according to the requirements. Wi-Fi is commonly used in homes for personal use. In offices, real-time data can be accessed using this wireless network.

WLAN hotspots – Many restaurants, hotels and other such commercial areas provide wi-fi hotspots for customers to access the internet. Also, no id and password is required in many cases to join the network.

Challenges in Wireless LAN

There are many challenges in wireless local area network which need to be resolved. Following are the main challenges:

Security Issues – There are various security vulnerabilities in Wireless LAN. The main security issues include – unauthorized attacks, denial-of-service attack and passive monitoring. In passive monitoring, an outsider can constantly monitor company’s information through his laptop/desktop. He can capture vital data and information from company and can retrieve company’s email ids and passwords. Denial-of-service attack can disable a company’s LAN. There is also a risk of unauthorized attack.

Interference – There is a risk of interference from unwanted radio signals which can disrupt the normal WLAN operation. This can cause delay in transmission and hence reduces the overall throughput. The devices in the network may not be able to access the WLAN leading to network latency and bad user experience.

Multipath Propagation – Multipath propagation can cause delay in information being transmitted. There will also be errors during modulation and demodulation. WLAN make use of certain protocols for retransmission of data if the data that the destination receives has error in it. Retransmission leads to lower performance.

Battery Limitations – A lot of battery power is consumed while accessing the wireless communication network. There are two modes to conserve the power. Doze Mode keeps the radio off and switched on periodically to check any unseen messages. Sleep Mode keeps the radio in standby mode.

Interoperability problems – There are interoperability issues also with WLAN when someone wants to work on multiple vendor devices.

WANET stands for wireless ad-hoc network. It is a decentralized wireless network. This type of network does not require pre-existing infrastructures like routers and access points for communication. Each node in the network participates in routing and forwards data for other nodes. Routing algorithm and network connectivity are the key parameters to determine which node will forward the data. The nodes in the network are free to move as the wireless ad-hoc network is self-configurable and dynamic. Thesis guidance and thesis help can be taken for this topic from networking experts. Masters students can go for this topic for their thesis.

Applications of Wireless ad-hoc Network(WANET)

There are various applications of wireless ad-hoc network due to its decentralized nature. Quick deployment and less configuration makes them suitable for installing in emergency situations. Following are the main applications of wireless ad-hoc network:

MANET – MANET stands for mobile ad-hoc network and is a network of mobile devices connected with each other. The mobile devices are infrastructure-less, self-configurable and self-organizing.

VANET – VANET stands for vehicular ad-hoc network. It is network for communication between the vehicles and other equipment on the road. It uses radio waves for communication.

SPAN – It stands for smart phone ad-hoc network. It is a peer-to-peer network between the smart phone devices.

Military – Army and military personnel use ad hoc network for long range communication. This is used for communication in remote areas and difficult terrains. UAV(Unmanned aerial network) is used by army to collect data and for situation sensing. Navy uses ad hoc network for communication with their counterparts on the land.

Wireless sensor network – Wireless sensor network is a wireless network that uses sensors to collect data. These sensors are connected to the wireless network. This data can be used for processing.

Disaster rescue – Wireless ad hoc network can be deployed in areas which have recently witnessed a disaster. This network is easy to deploy and configure and will help effectively in disaster rescue operations.

Advantages of WANET

High performance of the network

No extra infrastructure cost

Easy to deploy and configure

Disadvantages of WANET

Extremely dynamic topology

High degree of adaptability is not there

There are no central entities in the network

It is an IEEE 802.16 based wireless communication standards and provides multiple physical layer and media access control(MAC) options. It stands for wireless interoperability for Microwave Access. WiMax is designed to provide higher data rates upto 1 giga-bits/s. WiMax operates at higher speed, longer distance with more number of users than wi-fi. It is based on wireless MAN. WiMax Forum created the WiMax. People have knowledge of Wifi but do not know about WiMax. M.Tech students can choose this topic for their master’s thesis and do a research on that.

Importance of WiMax

WiMax is important due to the following reasons:

It can satisfy a variety of needs to extend the existing broadband capabilities.

It can deliver high bandwidth while keeping the cost of operation low.

It can meet the ever-increasing customer demands

It has more coverage area and better quality of services

It can be integrated with the existing technologies

WiMax services

WiMax provides following two types of services:

Non-line-of-sight – In this type of service, a small antenna is used to connect the computer to the WiMax tower. It uses a low frequency range from 2GHz to 11GHz.

Line-of-sight – In this service, a fixed antenna on the rooftop connects to the WiMax tower. It is more stable than non-line-of-sight and can send large amount of data with fewer errors.

Components of WiMax

A WiMax system has the following two components:

A WiMax tower – Same like cell-phone tower

A WiMax receiver – A small box

A WiMax tower connects to the high-speed internet through a wired connection of high bandwidth. It can also connect to the other WiMax tower using line-of-sight service. Through this connection with the tower, WiMax provides service to a large coverage area.

Features of WiMax

Coverage area upto 50 km from base station

Speed upto 70 megabits per second

Line-of-sight not required between the user and the base station

Frequency bands of 2-11 Ghz and 10-66 Ghz

Internet of Things is a wireless connection of devices for collection and sharing of data. In other words, it refers to ways by which internet is embedded to different devices. This technology is going to govern our life in near future. Every day-to-day activities will be controlled by the internet.

Features of IoT(Internet of Things)

Following are the feature of Internet of Things(IoT):

Artificial Intelligence – IoT will make our life ‘smart’ with the invention of various smart devices that can operate on their own using artificial intelligence algorithms.

Connectivity – IoT will create small networks between the devices at a cheaper rate. There will be new technologies in networking.

Sensors – Sensors are essential components of IoT enabled devices. Through sensors, surrounding data can be measured.

Active Engagement – IoT provides active product, content and service engagement.

Small Devices – Smaller but powerful devices are built which will have high scalability and versatility.

Advantages of Internet of Things(IoT)

Following are the main advantages of Internet of Things:

The customer engagement is improved with rich and effective engagement.

The technology is optimized in the sense that the devices used in the network and technology employed will be improved.

Internet of Things will lead to more effective management of resources thereby reducing waste.

The data collection will be more enhanced and accurate.

Disadvantages of Internet of Things(IoT)

There are security issues as the network is vulnerable to different kind of attacks.

There is risk of private information of user getting leaked.

The designing, deployment and maintenance of entire IoT network is complex.

ZigBee Technology

ZigBee is a low-cost and low-power wireless technology for machine-to-machine(M2M) and IoT networks. Mesh networking protocol is used for avoiding hub devices. The data is transferred at a low rate. It is based on IEEE 802.15 standard. ZigBee alliance maintain the specifications of ZigBee. Students from electronics and communication and networking field can opt this topic for their thesis.

Mesh Networking in ZigBee

ZigBee protocol uses mesh networking and architecture for communication. A mesh network make use of any one from full mesh topology and partial mesh topology.

In full mesh topology, each node is connected directly to all other nodes. In partial mesh topology, some of the nodes are connected to all other nodes while other nodes are connected to those nodes with which they want to exchange data. There are three type of nodes in ZigBee – coordinators, routers and end devices. The role of each node is different. Coordinators collect and store information. Routers are intermediates to coordinator and end devices. End devices are low-powered devices which interact with coordinator and router.

Advantages of ZigBee

The main advantages of ZigBee technology are:

Deployment is easy

Power consumption is low

Data transfer is secure

Innovation is rapid

These were some of the trending M.Tech thesis topics in wireless communication. Apart from these, other main research areas in wireless communication include 4G/5G technology, energy harvesting, optical fiber communication, and wireless sensor networks. Contact us for any type of thesis and research help from the field wireless communication for M.Tech and Ph.D.

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