This accessible guide contains everything you need to get up to speed on the theory and implementation of MIMO techniques.

Multiple-input, multiple-output (MIMO) communication technology has become a critical enabler for high-speed wireless communication systems. This edited volume, MIMO Communications – Fundamental Theory, Propagation Channels, and Antenna Systems, is a comprehensive resource for researchers, graduate students, and practicing engineers in wireless communication. The volume is divided into four parts that cover the foundations of wireless communications, antenna techniques, channel modeling, autonomous driving and radars. Experts in the field have authored chapters covering various topics, including capacity analysis of MIMO channels, antenna array design and beamforming techniques, channel modeling and estimation, and the applications of autonomous driving and radars. This book provides a detailed and accessible introduction to the latest research and practical applications in MIMO communication technology. It is an essential resource for anyone interested in learning about MIMO communication technology or looking to deepen their understanding of existing systems.

Driven by the desire to boost the quality of service of wireless systems closer to that afforded by wireline systems, space-time processing for multiple-input multiple-output (MIMO) wireless communications research has drawn remarkable interest in recent years. Exciting theoretical advances have been complemented by rapid transition of research results to industry products and services, thus creating a vibrant new area. Space-time processing is a broad area, owing in part to the underlying convergence of information theory, communications and signal processing research that brought it to fruition. This book presents a balanced and timely introduction to space-time processing for MIMO communications, including highlights of emerging trends, such as spatial multiplexing and joint transceiver optimization. Includes detailed coverage of wireless channel sounding, modelling, characterization and model validation. Provides state-of-the-art research results on space-time coding, including comprehensive tutorial coverage of orthogonal space-time block codes. Discusses important recent developments in spatial multiplexing, transmit beam-forming, pre-coding and joint transceiver design for the multi-user MIMO downlink using full or partial CSI. Illustrates all theory with numerous examples gleaned from cutting-edge research from around the globe. This valuable resource will appeal to engineers, developers and consultants involved in the design and implementation of space-time processing for MIMO communications. Its accessible format, amply illustrated with real world case studies, contains relevant, detailed advice for postgraduate students and researchers specializing in this field.

Advanced communication scenarios demand the development of new systemswhere antenna theory, channel propagation and communication models are seen from a common perspective as a way to understand and optimize the system as a whole. In this context, a comprehensive multiantenna formulation for multiple-input multiple-output systems is presented with a special emphasis on the connection of the electromagnetic and communication principles. Starting from the capacity for amultiantenna system, the book reviews radiation, propagation, and communicationmechanisms, paying particular attention to the vectorial, directional, and timefrequency characteristics of the wireless communication equation for low- and high-scattering environments. Based on the previous concepts, different space—time methods for diversity and multiplexing applications are discussed, multiantenna modeling is studied, and specific tools are introduced to analyze the antenna coupling mechanisms and formulate appropriate decorrelation techniques.Miniaturization techniques for closely spaced antennas are studied, and its fundamental limits and optimization strategies are reviewed. Finally, different practical multiantenna topologies for new communication applications are presented, and its main parameters discussed. A relevant feature is a collection of synthesis exercises that review the main topics of the book and introduces state-of-the art system architectures and parameters, facilitating its use either as a text book or as a support tool for multiantenna systems design. Table of Contents: Principles of Multiantenna Communication Systems / The Radio Channel for MIMO Communication Systems / Coding Theory for MIMO Communication Systems / Antenna Modeling for MIMO Communication Systems / Design of MPAs for MIMO Communication Systems / Design Examples and Performance Analysis of Different MPAs / References / List of Acronyms / List of Symbols / Operators and Mathematical Symbols

This book emphasis on multi-user MIMO communication. It covers a collection of the major topics and issues in multi-user MIMO systems. Recent Trends in Multi-user MIMO Communications provides a tutorial overview of the latest technologies and research keys related to multi-user communication. This book is composed of seven chapters, each written by a different set of authors. Features include: Fundamentals of multi-user MIMO communication, Random Beamforming in multi-user MIMO systems, LTE and LTE-Advanced framework, Interference cancellation in multi-user MIMO systems, Incorporation of multi-user capabilities in IEEE 802.11n/ac for WLAN systems, Physical layer security for multi-user MIMO communication, User selection based error probability of MIMO detector in multi-user MIMO systems.

Massive MIMO (multiple-input multiple-output) is considered as an heir of the multi-user MIMO technology and it has gained lots of attention from both academia and industry since the last decade. By equipping base stations (BSs) with hundreds of antennas in a compact array or a distributed manner, this new technology can provide very large multiplexing gains by serving many users on the same time-frequency resources and thereby bring significant improvements in spectral efficiency (SE) and energy efficiency (EE) over the current wireless networks. The transmit power, pilot training, and spatial transmission resources need to be allocated properly to the users to achieve the highest possible performance. This is called resource allocation and can be formulated as design utility optimization problems. If the resource allocation in Massive MIMO is optimized, the technology can handle the exponential growth in both wireless data traffic and number of wireless devices, which cannot be done by the current cellular network technology. In this thesis, we focus on the five different resource allocation aspects in Massive MIMO communications: The first part of the thesis studies if power control and advanced coordinated multipoint (CoMP) techniques are able to bring substantial gains to multi-cell Massive MIMO systems compared to the systems without using CoMP. More specifically, we consider a network topology with no cell boundary where the BSs can collaborate to serve the users in the considered coverage area. We focus on a downlink (DL) scenario in which each BS transmits different data signals to each user. This scenario does not require phase synchronization between BSs and therefore has the same backhaul requirements as conventional Massive MIMO systems, where each user is preassigned to only one BS. The scenario where all BSs are phase synchronized to send the same data is also included for comparison. We solve a total transmit power minimization problem in order to observe how much power Massive MIMO BSs consume to provide the requested quality of service (QoS) of each user. A max-min fairness optimization is also solved to provide every user with the same maximum QoS regardless of the propagation conditions. The second part of the thesis considers a joint pilot design and uplink (UL) power control problem in multi-cell Massive MIMO. The main motivation for this work is that the pilot assignment and pilot power allocation is momentous in Massive MIMO since the BSs are supposed to construct linear detection and precoding vectors from the channel estimates. Pilot contamination between pilot-sharing users leads to more interference during data transmission. The pilot design is more difficult if the pilot signals are reused frequently in space, as in Massive MIMO, which leads to greater pilot contamination effects. Related works have only studied either the pilot assignment or the pilot power control, but not the joint optimization. Furthermore, the pilot assignment is usually formulated as a combinatorial problem leading to prohibitive computational complexity. Therefore, in the second part of this thesis, a new pilot design is proposed to overcome such challenges by treating the pilot signals as continuous optimization variables. We use those pilot signals to solve different max-min fairness optimization problems with either ideal hardware or hardware impairments. The third part of this thesis studies a two-layer decoding method that mitigates inter-cell interference in multi-cell Massive MIMO systems. In layer one, each BS estimates the channels to intra-cell users and uses the estimates for local decoding within the cell. This is followed by a second decoding layer where the BSs cooperate to mitigate inter-cell interference. An UL achievable SE expression is computed for arbitrary two-layer decoding schemes, while a closed form expression is obtained for correlated Rayleigh fading channels, maximum-ratio combining (MRC), and largescale fading decoding (LSFD) in the second layer. We formulate a sum SE maximization problem with both the data power and LSFD vectors as optimization variables. Since the problem is non-convex, we develop an algorithm based on the weighted minimum mean square error (MMSE) approach to obtain a stationary point with low computational complexity. Motivated by recent successes of deep learning in predicting the solution to an optimization problem with low runtime, the fourth part of this thesis investigates the use of deep learning for power control optimization in Massive MIMO. We formulate the joint data and pilot power optimization for maximum sum SE in multi-cell Massive MIMO systems, which is a non-convex problem. We propose a new optimization algorithm, inspired by the weighted MMSE approach, to obtain a stationary point in polynomial time. We then use this algorithm together with deep learning to train a convolutional neural network to perform the joint data and pilot power control in sub-millisecond runtime. The solution is suitable for online optimization. Finally, the fifth part of this thesis considers a large-scale distributed antenna system that serves the users by coherent joint transmission called Cell-free Massive MIMO. For a given user set, only a subset of the access points (APs) is likely needed to satisfy the users' performance demands. To find a flexible and energy-efficient implementation, we minimize the total power consumption at the APs in the DL, considering both the hardware consumed and transmit powers, where APs can be turned off to reduce the former part. Even though this is a nonconvex optimization problem, a globally optimal solution is obtained by solving a mixed-integer second-order cone program (SOCP). We also propose low-complexity algorithms that exploit group-sparsity or received power strength in the problem formulation.