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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.
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
This book investigates and reviews recent advanced techniques and important applications in vehicular communications and networking (VCN) from a novel perspective of the combination and integration of VCN and connected vehicles, which provides a significant scientific and technical support for future 5G-based VCN. 5G-Enabled Vehicular Communications and Networking introduces vehicular channel characteristics, reviews current channel modeling approaches, and then provides a new generic geometry-based stochastic modeling approach for vehicle-to-everything (V2X) communications. The investigation of vehicular channel measurements and modeling provides fundamental supports for the VCN system design. Then, this book investigates VCN-vehicle combination from PHY and MAC layers, respectively. As for the PHY layer, many advanced techniques that can be effectively applied in VCN to counter the PHY challenges are introduced, including novel ICI cancellation methods, index modulated OFDM, differential spatial modulation, and energy harvesting relaying. As for the MAC layer, distributed and centralized MAC designs are analyzed and compared in terms of feasibility and availability. Specifically, distributed congestion control, D2D-enabled vehicular communications, and centralized data dissemination scheduling are elaborated, which can significantly improve the network performance in vehicular networks. Finally, considering VCN-vehicle integration, this book introduces several hot-topic applications in vehicular networks, including electric vehicles, distributed data storage, unmanned aerial vehicles, and security and privacy, which indicates the significance and development value of VCN-vehicle integration in future vehicular networks and our daily life. The primary audience for this book includes professionals and researchers working in the field of vehicular communications, intelligent transportation systems (ITS), and Internet of vehicles (IoV). Advanced level students studying electrical engineering will also find this book useful as a secondary textbook for related courses.
This monograph provides a survey on mmWave vehicular networks including channel propagation measurement, PHY design, and MAC design.
This book presents and develops comprehensive knowledge of vehicular channel characteristics and proper vehicular channel models. The studied topics contain the propagation characteristics of vehicular communications, such as: a time-frequency non-stationary single-input single-output (SISO) vehicle-to-vehicle (V2V) non-geometry stochastic model (NGSM); a space-time non-stationary massive multiple-input multiple-output (MIMO) V2V regular-shaped geometry-based stochastic model (RS-GBSM); and a space-time non-stationary massive MIMO V2V irregular-shaped geometry-based stochastic model (IS-GBSM). Each is introduced, with characteristics then discussed in detail. Finally, this book discusses future research directions to inspire further investigation in the field of vehicular channels from three different perspectives.
This book focuses on cellular Vehicle-to-Everything (C-V2X), currently the most promising wireless communication technology for Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Pedestrian (V2P), Vehicle-to-Network (V2N) and Vehicle-to-Cloud (V2C) communications. Because of its low latency and high reliability, C-V2X has become an essential enabling technology for Intelligent Transportation Systems (ITSs) and autonomous driving. This book begins by introducing readers to the research background and status quo of global development. Then, after analyzing the performance requirements of various V2X applications, the system architecture and technical standards are presented. The two evolving stages of C-V2X, i.e., LTE-V2X and NR-V2X, are introduced in detail. In addition, related technologies such as mobile edge computing, network slicing and high-precision positioning, C-V2X security, C-V2X spectrum requirements and planning, and industrial development and applications are introduced. In closing, the book discusses future applications of and technical challenges for C-V2X. This book is the first monograph dedicated to C-V2X, offering experts, researchers and engineers from the areas of IT/CT, intelligent transportation, intelligent and connected vehicles (ICVs) an in-depth understanding of C-V2X technology and standards, while also outlining related interdisciplinary research. The book can also be used as a reference resource for both undergraduate and graduate studies.
The Definitive, Comprehensive Guide to Cutting-Edge Millimeter Wave Wireless Design “This is a great book on mmWave systems that covers many aspects of the technology targeted for beginners all the way to the advanced users. The authors are some of the most credible scholars I know of who are well respected by the industry. I highly recommend studying this book in detail.” —Ali Sadri, Ph.D., Sr. Director, Intel Corporation, MCG mmWave Standards and Advanced Technologies Millimeter wave (mmWave) is today's breakthrough frontier for emerging wireless mobile cellular networks, wireless local area networks, personal area networks, and vehicular communications. In the near future, mmWave products, systems, theories, and devices will come together to deliver mobile data rates thousands of times faster than today's existing cellular and WiFi networks. In Millimeter Wave Wireless Communications, four of the field's pioneers draw on their immense experience as researchers, entrepreneurs, inventors, and consultants, empowering engineers at all levels to succeed with mmWave. They deliver exceptionally clear and useful guidance for newcomers, as well as the first complete desk reference for design experts. The authors explain mmWave signal propagation, mmWave circuit design, antenna designs, communication theory, and current standards (including IEEE 802.15.3c, Wireless HD, and ECMA/WiMedia). They cover comprehensive mmWave wireless design issues, for 60 GHz and other mmWave bands, from channel to antenna to receiver, introducing emerging design techniques that will be invaluable for research engineers in both industry and academia. Topics include Fundamentals: communication theory, channel propagation, circuits, antennas, architectures, capabilities, and applications Digital communication: baseband signal/channel models, modulation, equalization, error control coding, multiple input multiple output (MIMO) principles, and hardware architectures Radio wave propagation characteristics: indoor and outdoor applications Antennas/antenna arrays, including on-chip and in-package antennas, fabrication, and packaging Analog circuit design: mmWave transistors, fabrication, and transceiver design approaches Baseband circuit design: multi–gigabit-per-second, high-fidelity DAC and ADC converters Physical layer: algorithmic choices, design considerations, and impairment solutions; and how to overcome clipping, quantization, and nonlinearity Higher-layer design: beam adaptation protocols, relaying, multimedia transmission, and multiband considerations 60 GHz standardization: IEEE 802.15.3c for WPAN, Wireless HD, ECMA-387, IEEE 802.11ad, Wireless Gigabit Alliance (WiGig)
The book focuses on the advanced mmWave/Sub-terahertz ultra-massive MIMO wireless communications, which are regarded as a promising paradigm shift in future 5G beyond and even 6G. This is achieved by providing a comprehensive review of the rapidly developing field of massive MIMO communication, in-depth discussions on the impact of extremely large-scale antenna array, and detailed numerical simulation results on our proposed schemes. Several case studies are given after introducing basic communication system components, and the simulation codes are open sourced in our book, which shows the reproducibility of our models and methods and provides convenience for readers such as researchers, engineers, and graduate students in the fields of wireless communications.
Deep Learning (DL) is an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart and efficient resource allocation and intelligent distributed resource allocation methods. This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: (I) DL for vehicle safety and security: This part covers the use of DL algorithms for vehicle safety or security. (II) DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. This part covers how Intelligent vehicle networks require a flexible selection of the best path across all vehicles, adaptive sending rate control based on bandwidth availability and timely data downloads from a roadside base-station. (III) DL for vehicle control: The myriad operations that require intelligent control for each individual vehicle are discussed in this part. This also includes emission control, which is based on the road traffic situation, the charging pile load is predicted through DL andvehicle speed adjustments based on the camera-captured image analysis. (IV) DL for information management: This part covers some intelligent information collection and understanding. We can use DL for energy-saving vehicle trajectory control based on the road traffic situation and given destination information; we can also natural language processing based on DL algorithm for automatic internet of things (IoT) search during driving. (V) Other applications. This part introduces the use of DL models for other vehicle controls. Autonomous vehicles are becoming more and more popular in society. The DL and its variants will play greater roles in cognitive vehicle communications and control. Other machine learning models such as deep reinforcement learning will also facilitate intelligent vehicle behavior understanding and adjustment. This book will become a valuable reference to your understanding of this critical field.
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.