Large MIMO systems, with tens to hundreds of antennas, are a promising emerging communication technology. This book provides a unique overview of this technology, covering the opportunities, engineering challenges, solutions and state of the art of large MIMO test beds. There is in-depth coverage of algorithms for large MIMO signal processing, based on meta-heuristics, belief propagation and Monte Carlo sampling techniques, and suited for large MIMO signal detection, precoding and LDPC code designs. The book also covers the training requirement and channel estimation approaches in large-scale point-to-point and multi-user MIMO systems; spatial modulation is also included. Issues like pilot contamination and base station cooperation in multi-cell operation are addressed. A detailed exposition of MIMO channel models, large MIMO channel sounding measurements in the past and present, and large MIMO test beds is also presented. An ideal resource for academic researchers, next generation wireless system designers and developers, and practitioners in wireless communications.

This exclusive coverage of the opportunities, technological challenges, solutions, and state of the art of large MIMO systems provides an in-depth discussion of algorithms for large MIMO signal processing, suited for large MIMO signal detection, precoding and LDPC code designs. An ideal resource for researchers, designers, developers and practitioners in wireless communications.

The first complete guide to the physical and engineering principles of Massive MIMO, written by the pioneers of the concept.

The last ten years have seen a massive growth in the number of connected wireless devices. Billions of devices are connected and managed by wireless networks. At the same time, each device needs a high throughput to support applications such as voice, real-time video, movies, and games. Demands for wireless throughput and the number of wireless devices will always increase. In addition, there is a growing concern about energy consumption of wireless communication systems. Thus, future wireless systems have to satisfy three main requirements: i) having a high throughput; ii) simultaneously serving many users; and iii) having less energy consumption. Massive multiple-input multiple-output (MIMO) technology, where a base station (BS) equipped with very large number of antennas (collocated or distributed) serves many users in the same time-frequency resource, can meet the above requirements, and hence, it is a promising candidate technology for next generations of wireless systems. With massive antenna arrays at the BS, for most propagation environments, the channels become favorable, i.e., the channel vectors between the users and the BS are (nearly) pairwisely orthogonal, and hence, linear processing is nearly optimal. A huge throughput and energy efficiency can be achieved due to the multiplexing gain and the array gain. In particular, with a simple power control scheme, Massive MIMO can offer uniformly good service for all users. In this dissertation, we focus on the performance of Massive MIMO. The dissertation consists of two main parts: fundamentals and system designs of Massive MIMO. In the first part, we focus on fundamental limits of the system performance under practical constraints such as low complexity processing, limited length of each coherence interval, intercell interference, and finite-dimensional channels. We first study the potential for power savings of the Massive MIMO uplink with maximum-ratio combining (MRC), zero-forcing, and minimum mean-square error receivers, under perfect and imperfect channels. The energy and spectral efficiency tradeoff is investigated. Secondly, we consider a physical channel model where the angular domain is divided into a finite number of distinct directions. A lower bound on the capacity is derived, and the effect of pilot contamination in this finite-dimensional channel model is analyzed. Finally, some aspects of favorable propagation in Massive MIMO under Rayleigh fading and line-of-sight (LoS) channels are investigated. We show that both Rayleigh fading and LoS environments offer favorable propagation. In the second part, based on the fundamental analysis in the first part, we propose some system designs for Massive MIMO. The acquisition of channel state information (CSI) is very importantin Massive MIMO. Typically, the channels are estimated at the BS through uplink training. Owing to the limited length of the coherence interval, the system performance is limited by pilot contamination. To reduce the pilot contamination effect, we propose an eigenvalue-decomposition-based scheme to estimate the channel directly from the received data. The proposed scheme results in better performance compared with the conventional training schemes due to the reduced pilot contamination. Another important issue of CSI acquisition in Massive MIMO is how to acquire CSI at the users. To address this issue, we propose two channel estimation schemes at the users: i) a downlink "beamforming training" scheme, and ii) a method for blind estimation of the effective downlink channel gains. In both schemes, the channel estimation overhead is independent of the number of BS antennas. We also derive the optimal pilot and data powers as well as the training duration allocation to maximize the sum spectral efficiency of the Massive MIMO uplink with MRC receivers, for a given total energy budget spent in a coherence interval. Finally, applications of Massive MIMO in relay channels are proposed and analyzed. Specifically, we consider multipair relaying systems where many sources simultaneously communicate with many destinations in the same time-frequency resource with the help of a massive MIMO relay. A massive MIMO relay is equipped with many collocated or distributed antennas. We consider different duplexing modes (full-duplex and half-duplex) and different relaying protocols (amplify-and-forward, decode-and-forward, two-way relaying, and one-way relaying) at the relay. The potential benefits of massive MIMO technology in these relaying systems are explored in terms of spectral efficiency and power efficiency.

This book proposes promising mmWave solutions to promoting safe and reliable vehicular communications. The authors include topics such as channel estimation, multi-user transceiver design, and advanced index modulation. For channel estimation, unique channel properties and hybrid structures are first introduced, followed by the development of a doubly-sparse doubly-selective channel estimator. For multi-user transceiver design, the concept of hybrid block diagonalization (HBD) is first introduced, followed by a generic HBD-based transceiver design to maximize the system capacity. For advanced index modulation, the generalized beamspace modulation for uplink multi-user scenarios are first introduced, followed by the precoded beamspace modulation for the downlink. Finally, this book discusses open problems and future research directions to inspire further studies in the field of mmWave vehicular communications.

This SpringerBrief focuses mainly on the basic theory and applications of massive MIMO in 5G networks. The significance of massive MIMO for 5G or future communications is first briefly discussed. Then, the basic theory of massive MIMO technology is comprehensively analyzed, i.e., a variety of 5G scenarios and their improvements are described when massive MIMO is taken into account. Art physical-layer techniques and various networking techniques for interference mitigation and resource scheduling are introduced as well. This SpringerBrief also examines the selected applications of massive MIMO in 5G networks, i.e., massive MIMO-aided millimeter communications and energy transfer. The physical-layer design, multiple access control (MAC) mechanism and networking techniques are discussed for millimeter-wave communications aided by massive MIMO technology. Then, massive MIMO is covered for hybrid information and energy transfer. A downlink precoder and a uplink pilot scheme is proposed for single cell networks, and both non-cooperative and cooperative energy transfer in multi-cell are presented. Communication researchers in the area of MIMO technology, as well as researchers and practitioners working in millimeter communications and energy transfer seeking new research topics, and topic areas with communication system design, centralized and distributed algorithms, will find this brief useful as a reference. Advanced-level students studying communication engineering will also find this book useful as a secondary text.

The cellular network operators have witnessed significant growth in data traffic in the past few decades. This growth occurs due to the increases in the number of connected mobile devices, and further, the emerging mobile applications developed for rendering video-based on-demand services. As the frequency bandwidth for cellular communication is limited, significant effort was dedicated to improve the utilization of the available spectrum and increase the system performance via new technologies. For example, 3G and 4G networks were designed to facilitate high data traffic in cellular networks in past decades. Nevertheless, there is a necessity for new cellular network technologies to accommodate the ever-growing data traffic demand. 5G is behind the corner to deal with the tremendous data traffic requirements that will appear in cellular networks in the next decade. Massive MIMO (multiple-input-multi-output) is one of the backbone technologies in 5G networks. Massive MIMO originated from the concept of multi-user MIMO. It consists of base stations (BSs) implemented with a large number of antennas to increase the signal strengths via adaptive beamforming and concurrently serving many users on the same time-frequency blocks. As an outcome of using Massive MIMO technology, there is a notable enhancement of both sum spectral efficiency (SE) and energy efficiency (EE) in comparison with conventional MIMO based cellular networks. Resource allocation is an imperative factor to exploit the specified gains of Massive MIMO. It corresponds to properly allocating resources in the time, frequency, space, and power domains for cellular communication. Power control is one of the resource allocation methods to deliver high spectral and energy efficiency of Massive MIMO networks. Power control refers to a scheme that allocates transmit powers to the data transmitters such that the system maximizes some desirable performance metric. In the first part of this thesis, we investigate reusing the resources of a Massive MIMO system, for direct communication of some specific user pairs known as device-to-device (D2D) underlay communication. D2D underlay can conceivably increase the SE of traditional Massive MIMO systems by enabling more simultaneous transmissions on the same frequencies. Nevertheless, it adds additional mutual interference to the network. Consequently, power control is even more essential in this scenario in comparison with conventional Massive MIMO systems to limit the interference that is caused between the cellular network and the D2D communication, thereby enabling their coexistence. In this part, we propose a novel pilot transmission scheme for D2D users to limit the interference to the channel estimation phase of cellular users in comparison with the case of sharing pilot sequences for cellular and D2D users. We also introduce a novel pilot and data power control scheme for D2D underlaid Massive MIMO systems. This method aims at assuring that D2D communication enhances the SE of the network in comparison with conventional Massive MIMO systems. In the second part of this thesis, we propose a novel power control approach for multi-cell Massive MIMO systems. The new power control approach solves the scalability issue of two well-known power control schemes frequently used in the Massive MIMO literature, which are based on the network-wide max-min and proportional fairness performance metrics. We first explain the scalability issue of these existing approaches. Additionally, we provide mathematical proof for the scalability of our proposed method. Our scheme aims at maximizing the geometric mean of the per-cell max-min SE. To solve this optimization problem, we prove that it can be rewritten in a convex form and then be solved using standard optimization solvers.

Massive MIMO (Multiple-Input Multiple-Output) base stations have proven, both in theory and in practice, to possess many of the qualities that future wireless communication systems will require. They can provide equally high data rates throughout their coverage area and can concurrently serve multiple low-end handsets without requiring wider spectrum, denser base station deployment or significantly more power than current base stations. The main challenge of massive MIMO is the immense hardware complexity and cost of the base station—each element in the large antenna array needs to be individually controllable and therefore requires its own radio chain. To make massive MIMO commercially viable, the base station has to be built from inexpensive simple hardware. In this thesis, it is investigated how the use of low-end power amplifiers and analog-to-digital converters (ADCs) affects the performance of massive MIMO. In the study of the signal distortion from low-end amplifiers, it is shown that in-band distortion is negligible in massive MIMO and that out-of-band radiation is the limiting factor that decides what power efficiency the amplifiers can be operated at. A precoder that produces transmit signals for the downlink with constant envelope in continuous time is presented to allow for highly power efficient low-end amplifiers. Further, it is found that the out-of-band radiation is isotropic when the channel is frequency selective and when multiple users are served; and that it can be beamformed when the channel is frequency flat and when few users are served. Since a massive MIMO base station radiates less power than today's base stations, isotropic out-of-band radiation means that low-end hardware with poorer linearity than required today can be used in massive MIMO. It is also shown that using one-bit ADCs—the simplest and least power-hungry ADCs—at the base station only degrades the signal-to-interference-and-noise ratio of the system by approximately 4 dB when proper power allocation among users is done, which indicates that massive MIMO is resistant against coarse quantization and that low-end ADCs can be used.

mmWave Massive MIMO: A Paradigm for 5G is the first book of its kind to hinge together related discussions on mmWave and Massive MIMO under the umbrella of 5G networks. New networking scenarios are identified, along with fundamental design requirements for mmWave Massive MIMO networks from an architectural and practical perspective. Working towards final deployment, this book updates the research community on the current mmWave Massive MIMO roadmap, taking into account the future emerging technologies emanating from 3GPP/IEEE. The book's editors draw on their vast experience in international research on the forefront of the mmWave Massive MIMO research arena and standardization. This book aims to talk openly about the topic, and will serve as a useful reference not only for postgraduates students to learn more on this evolving field, but also as inspiration for mobile communication researchers who want to make further innovative strides in the field to mark their legacy in the 5G arena. - Contains tutorials on the basics of mmWave and Massive MIMO - Identifies new 5G networking scenarios, along with design requirements from an architectural and practical perspective - Details the latest updates on the evolution of the mmWave Massive MIMO roadmap, considering future emerging technologies emanating from 3GPP/IEEE - Includes contributions from leading experts in the field in modeling and prototype design for mmWave Massive MIMO design - Presents an ideal reference that not only helps postgraduate students learn more in this evolving field, but also inspires mobile communication researchers towards further innovation