This book discusses the latest channel coding techniques, MIMO systems, and 5G channel coding evolution. It provides a comprehensive overview of channel coding, covering modern techniques such as turbo codes, low-density parity-check (LDPC) codes, space–time coding, polar codes, LT codes, and Raptor codes as well as the traditional codes such as cyclic codes, BCH, RS codes, and convolutional codes. It also explores MIMO communications, which is an effective method for high-speed or high-reliability wireless communications. It also examines the evolution of 5G channel coding techniques. Each of the 13 chapters features numerous illustrative examples for easy understanding of the coding techniques, and MATLAB-based programs are integrated in the text to enhance readers’ grasp of the underlying theories. Further, PC-based MATLAB m-files for illustrative examples are included for students and researchers involved in advanced and current concepts of coding theory.
Next-generation wireless networks aim to enable order-of-magnitude increases in connectivity, capacity, and speed. Such a goal can be achieved in part by utilizing larger frequency bandwidth or by deploying denser base stations. As the number of wireless devices is exploding, however, it is inevitable that multiple devices communicate over the same time and same spectrum. Consequently, improving the spectral efficiency in wireless networks with multiple senders and receivers becomes the key challenge. This dissertation investigates low-complexity channel coding techniques that implement canonical random coding schemes in network information theory, such as universal channel coding, superposition coding, rate-splitting, successive cancellation, simultaneous decoding, decode-forward relaying, compress-forward relaying, and Slepian--Wolf coding. In representative communication scenarios, such as compound channels, interference channels, broadcast channels, and relay channels, the proposed channel coding techniques achieve the best known information theoretic performance, some utilizing the recently invented polar codes and some making use of the commercial off-the-shelf codes, e.g., turbo and LDPC codes. These techniques have a potential to become important building blocks towards a general theory of channel coding techniques for the next-generation high-spectral-efficiency, low-power, broad-coverage wireless communication.
Next-generation wireless communication systems will have to deal with an unprecedented number of communicating users and devices while enabling orders-of-magnitude of performance improvement in speed and connectivity. With the increasingly complex network structure and the high spectral efficiency requirements, it becomes extremely inefficient to rely on traditional channel coding paradigms that do not take into account the structure of the network and its inherent properties. Unlike conventional channel coding schemes that are designed under the assumption of a single sender and a single receiver communicating over a memoryless channel, this dissertation investigates low-complexity channel coding techniques that take advantage of the number of communicating devices in a network and the inherent memory in the channel. In communication over networks, low-complexity channel coding schemes that achieve the best known information theoretic performance are constructed starting from simple coding blocks. In communication over channels with memory, practical channel coding techniques that exploit the memory in the channel are developed. In both cases, the proposed coding techniques have the potential of addressing the increasing-spectral-efficiency requirement in next-generation wireless communication systems.
This book provides a comprehensive overview of the subject of channel coding. It starts with a description of information theory, focusing on the quantitative measurement of information and introducing two fundamental theorems on source and channel coding. The basics of channel coding in two chapters, block codes and convolutional codes, are then discussed, and for these the authors introduce weighted input and output decoding algorithms and recursive systematic convolutional codes, which are used in the rest of the book. Trellis coded modulations, which have their primary applications in high spectral efficiency transmissions, are then covered, before the discussion moves on to an advanced coding technique called turbocoding. These codes, invented in the 1990s by C. Berrou and A. Glavieux, show exceptional performance. The differences between convolutional turbocodes and block turbocodes are outlined, and for each family, the authors present the coding and decoding techniques, together with their performances. The book concludes with a chapter on the implementation of turbocodes in circuits. As such, anyone involved in the areas of channel coding and error correcting coding will find this book to be of invaluable assistance.
The communication chain is constituted by a source and a recipient, separated by a transmission channel which may represent a portion of cable, an optical fiber, a radio channel, or a satellite link. Whatever the channel, the processing blocks implemented in the communication chain have the same foundation. This book aims to itemize. In this first volume, after having presented the base of the information theory, we will study the source coding techniques with and without loss. Then we analyze the correcting codes for block errors, convutional and concatenated used in current systems.
A comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks. The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities. Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence. The author – a noted expert on the topic – covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations. Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource: Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks Covers a range of topics from architecture and optimization to adaptive resource allocations Reviews state-of-the-art machine learning based solutions for network coverage Includes an overview of the applications of machine learning algorithms in future wireless networks Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end (DFE) and radio-frequency (RF) processing Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.
The high level of technical detail included in standards specifications can make it difficult to find the correlation between the standard specifications and the theoretical results. This book aims to cover both of these elements to give accessible information and support to readers. It explains the current and future trends on communication theory and shows how these developments are implemented in contemporary wireless communication standards. Examining modulation, coding and multiple access techniques, the book is divided into two major sections to cover these functions. The two-stage approach first treats the basics of modulation and coding theory before highlighting how these concepts are defined and implemented in modern wireless communication systems. Part 1 is devoted to the presentation of main L1 procedures and methods including modulation, coding, channel equalization and multiple access techniques. In Part 2, the uses of these procedures and methods in the wide range of wireless communication standards including WLAN, WiMax, WCDMA, HSPA, LTE and cdma2000 are considered. An essential study of the implementation of modulation and coding techniques in modern standards of wireless communication Bridges the gap between the modulation coding theory and the wireless communications standards material Divided into two parts to systematically tackle the topic - the first part develops techniques which are then applied and tailored to real world systems in the second part Covers special aspects of coding theory and how these can be effectively applied to improve the performance of wireless communications systems
Channel coding lies at the heart of digital communication and data storage, and this detailed introduction describes the core theory as well as decoding algorithms, implementation details, and performance analyses. In this book, Professors Ryan and Lin provide clear information on modern channel codes, including turbo and low-density parity-check (LDPC) codes. They also present detailed coverage of BCH codes, Reed-Solomon codes, convolutional codes, finite geometry codes, and product codes, providing a one-stop resource for both classical and modern coding techniques. Assuming no prior knowledge in the field of channel coding, the opening chapters begin with basic theory to introduce newcomers to the subject. Later chapters then extend to advanced topics such as code ensemble performance analyses and algebraic code design. 250 varied and stimulating end-of-chapter problems are also included to test and enhance learning, making this an essential resource for students and practitioners alike.
This book clearly describes the leading techniques for channel coding. An advanced tutorial greatly improves the reader's understanding of the material. Rigorous analytical and theoretical examples are provided along with sample problems and solutions. The latest research findings on new techniques in channel coding are also included.
In order to adapt to the ever-increasing demands of telecommunication needs, today’s network operators are implementing 100 Gb/s per dense wavelength division multiplexing (DWDM) channel transmission. At those data rates, the performance of fiberoptic communication systems is degraded significantly due to intra- and inter-channel fiber nonlinearities, polarization-mode dispersion (PMD), and chromatic dispersion. In order to deal with those channel impairments, novel advanced techniques in modulation and detection, coding and signal processing are needed. This unique book represents a coherent and comprehensive introduction to the fundamentals of optical communications, signal processing and coding for optical channels. It is the first to integrate the fundamentals of coding theory with the fundamentals of optical communication.
