5G and Beyond Wireless Systems

The conventional single-input-single-output (SISO) system exhibits a major problem of having limited throughput, coverage and data rate. These drawbacks can be overcome by smart antenna technologies such as multiple-input-multiple-output (MIMO) technologies and beamforming antenna array. In MIMO systems, multiple antennas are used for both transmission and reception, which is the crucial architecture for emerging wireless communication systems. MIMO technologies increase the system throughput thanks to the utilization of multiple data streams for transmitter and receiver ends. In current fourth-generation (4G) communication systems, the \(n\times n\) MIMO architecture has the number of antennas n as 2 or 4. But in the emerging fifth-generation (5G) communication systems, the number of antennas increases to \(n>8\). This practice of using a greater number of antennas solves the problem of limited throughput, coverage and data rate. MIMO antenna design is not a minor task; rather, it has to be done carefully in order to uphold some performance metrics.

Massive multiple-input multiple-output (MIMO) has been considered as one of the most promising technologies for addressing high data rates and capacity for cellular networks in the fifth generation and beyond. In massive MIMO, the base station (BS) requires complete and accurate channel state information (CSI) for realizing the benefits of massive MIMO technology. The CSI can be obtained either through feedback from users or channel reciprocity technique. The use of non-orthogonal pilot sequences, sent by the users for determining CSI, has been the main source of pilot contamination in the uplink data communications. Various pilot contamination scenarios and their possible solutions have been extensively studied in the last few years. In this chapter, recent works in the area of mitigating the effects of pilot contamination have been presented and analyzed. Some of the higher throughput resulting algorithms and their capabilities in eliminating the effects of pilot contamination have been studied in detail.

Devising low-complexity data detection techniques is one of the fundamental challenges in the uplink of massive multiple-input multiple-output (MIMO) wireless systems. Linear detection techniques such as zero-forcing (ZF) and minimum mean square error (MMSE) are shown to achieve near-optimal bit-error-rate (BER) performance in such systems. However, ZF/MMSE technique requires inversion of large-dimensional matrices which makes them practically infeasible. This motivates the development of alternate low-complexity inversionless detection techniques which are capable of achieving BER performance close to that of the ZF/MMSE detectors with comparatively less computations. Recently, there is an upsurge in research toward solving this crucial issue in massive MIMO systems. In particular, several detection algorithms have been proposed in the literature, which provides a better trade-off between BER performance and computational complexity. This chapter discusses the fundamentals of massive MIMO detection and also provides an overview of some of the recent state-of-the-art detection techniques. Simulation results on BER performance and computational complexity of these algorithms are also compared to draw useful insights. Furthermore, research scopes in massive MIMO detection are also discussed to provide possible research directions in the field.

Index modulation (IM) is one of the emerging techniques for enabling beyond 5G (B5G) wireless communications. It has the potential to meet the stringent energy efficiency (EE) and spectral efficiency (SE) requirements of B5G systems with better error rate performance over the conventional techniques. IM relaxes the need for activating all the resources at the transmitter to transmit the information, thereby allowing low-complexity transmitter architecture designs. The key idea behind IM is to encode the information in the indices of the available resources at the transmitter such as antenna, sub-carriers in orthogonal frequency division multiplexing, time-slots, and radio frequency (RF) mirrors. Massive-MIMO is another such promising technique which provides unprecedented growth in both EE and SE for B5G wireless systems. Spatial multiplexed massive-MIMO is shown to achieve the unbelievable capacity gains over the conventional MIMO systems. However, achieving such gains requires dedicated signal processing resources for each antenna which increase the cost, area, and power-requirement at the transmitter. Interestingly, through IM in massive-MIMO, exceptionally high EE and SE can be achieved with minimal use of the available resources. Recently, multi-dimensional IM (MIM), wherein multiple resources are indexed simultaneously during transmission to enhance the SE further, has attracted researchers and experts from both academia and industry. In this chapter, we discuss different IM and MIM techniques, their representations, and advantages in B5G communications. In particular, we discuss spatial modulation, generalized spatial modulation, media-based modulation, and their possible combinations as MIM in detail. We also shed the light on the maximum-likelihood detection of information symbols in such systems.

Next-generation wireless networks require higher spectral efficiency and lower latency to meet the demands of various upcoming applications. Recently, non-orthogonal multiple access (NOMA) schemes are introduced in the literature for 5G and beyond. Various forms of NOMA are considered like power domain, code domain, pattern division multiple access, etc. to enhance the spectral efficiency of wireless networks. In this chapter, we introduce the code domain-based sparse code multiple access (SCMA) NOMA scheme to enhance the spectral efficiency of a wireless network. The design and detection of an SCMA system are analyzed in this chapter. Also, the method for codebooks design and its impact on system performance are highlighted. A hybrid multiple access scheme is also introduced using both code-domain and power-domain NOMA. Furthermore, simulation results are included to show the impact of various SCMA system parameters.

This paper presents a practical scenario of non-orthogonal multiple access (NOMA) networks. In particular, a two-way NOMA network with hardware impairments is analyzed. To facilitate the system performance analysis, closed-from expressions are derived for the outage probability and throughput. Furthermore, to provide insights of performance, an asymptotic expression is introduced for the case of high SNR and then limitation of the system under hardware impairments is determined. Since successive interference cancelation (SIC) is adopted at each receiver, SIC affects the detection performance. As such, we consider both imperfect SIC (ipSIC) and perfect SIC cases. In addition, and we further compare system performance of NOMA with traditional orthogonal multiple access (OMA). Finally, to verify the analytical results Monte Carlo simulations presented.

The exponential growth of wireless multimedia services and devices expect more spectrums to support the quality of service (QoS) of the fifth-generation (5G) and beyond cellular networks. The available large underutilized radio resources in millimeter wave (mmWave) have the potential to solve limited radio resource issues of the existing cellular bands. Recent studies suggest that densely deployed mmWave base stations with directional beamforming capability can be used for cellular systems. However, the exploitation of mmWave channels for the cellular network also introduces new challenges because of the inconsistent transmission behavior of the mmWave channel. Despite the problems raised for the mmWave signals, there are recent breakthroughs that make the mmWave viable for cellular communications, and the cellular network infrastructure should be expanded accordingly. In this regard, this chapter presents a review of issues associated with the mmWave-based cellular networks. Furthermore, key concepts and useful insights related to the mmWave-based cellular network design are also provided. Besides, a stochastic geometry-based theoretical framework is developed to account for the effect of the exploitation of mmWave for the cognitive-based multi-tier cellular networks. In this scheme, when conventional cellular channels are vacant, low power nodes operate in the traditional cellular band, and they are served by the mmWave band when channels are busy. Outage probability, area spectral efficiency, and energy efficiency are theoretically calculated for the system. Numerical results demonstrate that the proposed approach offers a significant improvement in outage performance as compared to cognitive radio-based multi-tier networks.

In the last decade, the wireless inband full-duplex (FD) communications have been extensively studied to make a better use of available spectral resources. The FD communication allows the simultaneous transmission and reception on the same frequency band within the same device, which theoretically doubles the spectral efficiency. Moreover, FD multiple user communication assisted by spectral efficient bidirectional relays provides better coverage, better diversity gains, and improved capacity performance. This chapter examines the recent works done in the area of mobile multi-user FD relaying systems from the physical layer perspective. This chapter begins with the comparison of traditional half duplex techniques with the recently developed FD radios and the modeling of residual self-interference at the FD node. Further, recent works related to the half-duplex and/or FD, two-way and/or one-way relaying where the impact of user mobility is taken into account are discussed in detail. Moreover, this chapter surveys the multi-user systems, and the performances of various multi-user scheduling schemes are compared in detail. Finally, this chapter includes some applications of FD relaying with short-packet communication for its possible applications in mission-critical applications such as virtual reality, autonomous driving, telemedicine, etc.

The stringent criteria of the fifth-generation (5G) radio networks have sparked research on waveforms beyond the fourth-generation mobile radio networks (4G). Recently, filtered multicarrier (FMC) transmission-based techniques have been proposed as the waveforms for 5G systems. These techniques have their own merits and demerits. Some major demerits still remain unsolved in the design of 5G waveforms. The high peak-to-average power ratio (PAPR) of the transmitted FMC signals is one such demerit. The peak-to-average power ratio is the ratio of the peak power to the average power in the waveform. An extensive amount of research has been carried out in literature to reduce the PAPR value for the filtered multicarrier system. This chapter presents a comprehensive summary of the various filter techniques as well as methods for reducing the PAPR. Further, a comparative performance analysis of these techniques is presented to elucidate their merits. Non-orthogonal waveforms such as generalized frequency division multiplexing (GFDM), filter bank multicarrier (FBMC), biorthogonal frequency-division multiplexing (BFDM), and universal filtered multicarrier (UFMC) have been considered. The results assist in making the decision of proper waveforms among many to suit different next-generation mobile communication systems.

In today’s modern communications, with the evolution of various applications, the demand for data rate is increasing exponentially at the cost of huge consumption of available resources. It has been recorded that the communication networks dissipate nearly 1% of the worldwide total power consumption which results in millions of tons of \(\text {CO}_2\) emission due to their production, thereby causing various environmental health hazards. The optimal utilization of available resources that can balance the present coexisting problem without any compromise on the high throughput demand paves the way for the next-generation green 5G wireless networks. In this chapter, we study the minimization of total power consumption while satisfying the desired coverage of the user equipments (UEs) to provide the minimum throughput over the network. In this regard, the deployment of base stations (BSs), their number, and transmit power are optimized in two scenarios (i) when the number of UEs is large in 5G wireless network and (ii) when a moderate number of UEs are distributed over the field.

There is a massive increase in the number of global users for wireless services such as broadband, cellular, and television. Thus, there is a requirement of uninterrupted connectivity with higher data rates. However, the current static frequency allocation method is unable to serve such a massive increase in users and their desired services. As a result, innovative methods should be used or developed, which can efficiently exploit the available spectrum with the minimum requirement of extra resources. Cognitive radio is one such attractive solution that exploits the spectrum efficiently by making opportunistic usage of the frequency bands that are not being used by licensed or primary users. Cognitive radio has the capability to measure, learn, and sense the parameters related to the radio environment, available spectrum, and power in the spectrum. In this chapter, first, we discuss the methods of spectrum sharing between the primary and secondary users. Next, we introduce different types of spectrum sensing schemes, which are classified under blind and non-blind techniques. We also examine different noise models, including Gaussian and color noise. In addition, cooperative spectrum sensing is also discussed, followed by the implementation of various algorithms on software-defined radio. At last, cognitive radio with full-duplex communication is discussed, which is considered in the context of 5G and beyond communication.

To address spectrum underutilization and energy constraint in wireless communication technologies, cognitive radio networks (CRN) incorporated with energy-harvesting (EH) ability is an evergreen solution. The dynamic behavior of the primary user (PU) activity is a primary component that affects the performance of a CRN. In this chapter, we investigate the impact of distribution functions on the performance of an energy harvesting enabled cooperative CRN. We consider the two most relevant distribution functions, namely Weibull and Erlang distributions, to characterize the PU behavior in a prediction-based sensing under a cooperative EH-CRN. Cooperative CRN offers better reliability of event detection, which results in efficient spectrum utilization. In this chapter, we consider a centralized cooperative EH-CRN whereby each cognitive radio (CR) node has the capability of scavenging energy from radio frequency (RF) or non-RF sources depending on a combined decision taken by the fusion center (FC). We use conventional and estimation-based energy detection schemes in our analysis. Analytical formulae for the detection probability, harvested energy, normalized throughput, and energy penalty are established, employing OR fusion rule. The impact of prediction error, number of cooperative CR nodes, number of frames, and collision constraint on energy harvesting and normalized throughput is also studied. Simulations are performed, and a thorough, comprehensive comparison of the results is presented. A detailed comparative analysis for both Weibull and Erlang distributions is also presented. The results show that both distributions perform better than the conventional exponential distribution in a centralized cooperative EH-CRN and signify the usability of the model in designing practical systems.

Spectrum handoff (SH) is a vital process to guarantee seamless and effective services of secondary users (SUs) in cognitive radio networks (CRNs). SH delay has a negative impact on the performance of SUs. For simplicity, the PRP M/G/1 queuing model is used in literature to evaluate the SH delay parameters of CUs in a CRN. However, the design of an infinite buffer size queue in a real-time tele-traffic system is not feasible. We present pre-emptive resume priority (PRP) M/G/1/K queuing model comprising of three priority queues: primary user (PU) queue for higher priority PUs, interrupted user (IU) queue for moderate priority interrupted SUs and SU queue for lower priority newly arrived SUs, to derive the SH performance metrcs such as blocking probability and cumulative handoff delay (CHD) of SUs. This chapter analyses the impact of buffer length (K) on blocking probability and CHD for various proactive SH schemes: non-switching, switching and random SH schemes in CRNs. We present and summarise the detailed comparison of results for blocking probability and CHD in terms of PUs’ arrival rate and mobility parameter of spectrum holes for different K under PRP M/G/1/K queuing network model. Results show that the blocking probability decreases and the CHD increases with increasing value of K. For an optimal value of K, the proposed model offers similar performance to the PRP M/G/1 queuing network model.

The fifth-generation (5G) wireless systems have recently reached to the deployment stage, and research toward next-generation wireless communications has already started. The integration of Internet of things (IoT) to satellite networks is one of the key focuses in 5G and beyond systems. Since the spectrum resources are not abundant to accommodate billions of IoT devices, the cognitive satellite-terrestrial networks supporting IoT communications are of great importance. In this chapter, we present the outage performance analysis of a cognitive overlay multi-user satellite-terrestrial network (OMSTN) where a primary satellite communicates with a selected terrestrial receiver via a secondary IoT network in the presence of interference from extra-terrestrial sources (ETSs) and terrestrial sources (TSs). Hereby, we consider the following two cases for system performance analysis: (a) Case 1: when interference occurs due to TSs only; (b) Case 2: when interference occurs due to both ETSs and TSs. We specifically derive the tight closed-form outage probability (OP) expressions of both the primary satellite and secondary IoT networks. We further carry out the asymptotic high signal-to-noise ratio (SNR) analysis to obtain the diversity order of both satellite and IoT networks. For asymptotic analysis, we consider two scenarios, namely when interferers’ power is fixed and when it varies proportionally to the transmit power of satellite and IoT nodes. We further devise a scheme for the adaptive power splitting factor under a guaranteed quality-of-service (QoS) of the primary satellite network. We provide various insights on the considered OMSTN based on our analysis and numerical results.

The proliferation of mobile devices and data-hungry applications running on them leads to a massive growth of wireless data traffic. Supporting this ever-increasing data demands and communication rate requires reconsideration of the existing cellular network architecture. Device-to-device (D2D) communications, which allow two mobile devices in the proximity to communicate with each other, emerge as a potential solution to this challenge. It provides multifold gains in terms of transmission-rate gain, frequency-reuse gain, coverage-gain, and hop-gain. However, extensive deployment of D2D communications in the cellular networks poses several intrinsic challenges, such as severe interference to the primary cellular users, rapid battery depletion of D2D transmitters in relaying scenarios. Therefore, this chapter discusses various challenges in supporting D2D communications. It then surveys various existing resource allocation schemes to address these challenges. It also provides the achievable performance over different fading channels and computational complexity of the power allocation schemes.

Vehicular communication is a key technology for realizing automatic vehicle systems, improving the safety of drivers and passengers, and providing support for smart traffic handling. In addition, it can help the drivers to take appropriate decisions and support the modernization of different operations of vehicles along with some useful applications for the passengers. In this chapter, we discuss various forms of vehicular communications, e.g., vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-pedestrian communications. We also highlight the recent advancements done for different modes of vehicular communications, which can be summarized as vehicle-to-everything (V2X) communications. Moreover, we identify the potential research directions to address the challenges for bringing V2X communications into operation. To illustrate the impact of mobility, we provide mathematical modeling of the node’s mobility and analyze the system performance in terms of outage probability and system throughput under Rayleigh fading environment.

The surge in demand for faster, reliable, and inter-operable networks leads to the development of 5G networks. The resultant increase in the numbers of services, heterogeneous devices with quality of service assurance requires the infrastructure to be further optimized. A sub-optimal infrastructure can be tuned through manual or rule-based optimization, but to further realize the full potential of the 5G network, the change should be based on the current scenario and traffic pattern in the network. The recent developments in machine learning algorithms where the models are capable of learning from the data itself have proved their potential by minimizing the manual intervention for optimal performance. In this chapter, authors identify the areas where the problems faced in 5G infrastructure can be modeled as machine learning problems, which is followed by the introduction of well-explored machine learning models for solving these problems.