The on-going data revolution demands storage systems that can store very large quantities of data while being fast, reliable and cheap. Emerging storage technologies such as flash and granular media offer improved densities, faster access times, and are more power-efficient than conventional hard disk drives. The primary drawback associated with these new devices is their high error rate, caused by difficulties in programming, voltage drift, and wear-out. Coding methods used in existing storage applications are based on symmetric, Hamming-type metrics. However, when used in new memory devices, these traditional approaches result in costly overprovisioning. In this work, we present advanced coding-theoretic techniques applicable to modern storage devices that exploit the asymmetries in the underlying physical operations for improved performance. In many cases of interest, the results in this thesis represent the state of the art. Taken collectively, our results can help enable all modern, data-intensive technologies that require reliably storing large quantities of data.
Channel coding theory offers advanced mathematical techniques that have proven to be highly effective at improving the reliability of traditional communication systems such as wireless communication, storage in memories, and many more. However, modern data driven applications such as blockchains and quantum communications encounter a new set of challenges resulting in new metrics of concerns, e.g., storage requirements, communication costs, security, data rates, etc., compared to traditional systems. These new metrics necessitate new and specialized channel code designs to improve the performance of these systems. In this dissertation, we aim to mitigate the challenges encountered in certain widely used data-driven applications viz. blockchains and quantum communications by designing specialized channel codes that are tailor-made for each specific application. The first line of the dissertation is focused on specialized Low-Density Parity-Check (LDPC) code design to mitigate challenges present in blockchain systems. These systems are known to suffer from a security vulnerability known as Data Availability (DA) Attacks where system users accept an invalid block with unavailable portions. Existing work focused on utilizing random LDPC codes and 2D Reed-Solomon (2D-RS) codes to mitigate DA attacks. Although effective, these codes are not necessarily optimal for this application, especially for blockchains with small block sizes. For these types of blockchains, we propose a co-design of specialized LDPC codes and code word sampling strategies to result in good system performance in terms of DA detection probability and communication cost. We devise our co-design techniques to tackle adversaries of varying strengths and demonstrate that they result in a higher probability of detection of DA attacks and lower communication cost compared to approaches in earlier literature. The second line of the dissertation is focused on specialized polar code design to mitigate DA attacks in blockchains with large block sizes. Previously used 2D-RS codes and LDPC codes are difficult to apply to blockchains with large block sizes due to their large decoding complexity and coding fraud proof size (2D-RS codes), and intractable code guarantees for large code lengths (LDPC codes). To mitigate DA attacks in blockchains with large block sizes, we propose a novel data structure called Graph Coded Merkle Tree (GCMT): a Merkle tree encoded using the encoding graph of polar codes. Additionally, we propose a specialized polar code design algorithm for the GCMT. We demonstrate that the GCMTbuild using the above specialized polar codes simultaneously performs well in the various performance metrics relevant to DA attacks at large block sizes including DA detection probability, communication cost, tractable code guarantees, and decoding complexity. The third line of the dissertation is focused on an important application in quantum communication known as Quantum Key Distribution (QKD). QKD aims to provide private keys to multiple users at a large key generation rate. LDPC codes have been previously utilized to extract private keys in QKD. However, the existing LDPC codes do not fully utilize the properties of the QKD channel to optimize the key rates. In this dissertation, we propose novel and specialized channel coding techniques to result in high key generation rates in QKD systems. Firstly, we propose a joint code rate and LDPC code design algorithm that is tailored to use the properties of the QKD channel for high key rates. Secondly, we propose an interleaved decoding algorithm to extract the private key from raw quantum data. We demonstrate that the above techniques significantly improve the private key generation rate in QKD systems compared to approaches in earlier literature.
Consolidating knowledge on Joint Source-Channel Coding (JSCC), this book provides an indispensable resource on a key area of performance enhancement for communications networks Presenting in one volume the key theories, concepts and important developments in the area of Joint Source-Channel Coding (JSCC), this book provides the fundamental material needed to enhance the performance of digital and wireless communication systems and networks. It comprehensively introduces JSCC technologies for communications systems, including coding and decoding algorithms, and emerging applications of JSCC in current wireless communications. The book covers the full range of theoretical and technical areas before concluding with a section considering recent applications and emerging designs for JSCC. A methodical reference for academic and industrial researchers, development engineers, system engineers, system architects and software engineers, this book: Explains how JSCC leads to high performance in communication systems and networks Consolidates key material from multiple disparate sources Is an ideal reference for graduate-level courses on digital or wireless communications, as well as courses on information theory Targets professionals involved with digital and wireless communications and networking systems
Coding for Channels with Feedback presents both algorithms for feedback coding and performance analyses of these algorithms, including analyses of perhaps the most important performance criterion: computational complexity. The algorithms are developed within a single framework, termed the compressed-error-cancellation framework, where data are sent via a sequence of messages: the first message contains the original data; each subsequent message contains a source-coded description of the channel distortions introduced on the message preceding it. Coding for Channels with Feedback provides an easily understood and flexible framework for deriving low-complexity, practical solutions to a wide variety of feedback communication problems. It is shown that the compressed-error-cancellation framework leads to coding schemes with the lowest possible asymptotic order of growth of computations and can be applied to discrete memoryless channels, finite state channels, channels with memory, unknown channels, and multiple-access channels, all with complete noiseless feedback, as well as to channels with partial and noisy feedback. This framework leads to coding strategies that have linear complexity and are capacity achieving, and illustrates the intimate connection between source coding theory and channel coding theory. Coding for Channels with Feedback is an excellent reference for researchers and communication engineers in the field of information theory, and can be used for advanced courses on the topic.
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.
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 features research papers presented at the International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2022) held at Institute of Engineering & Management, Kolkata, India, during February 23–25, 2022. The book is organized in three volumes and includes high-quality research work by academicians and industrial experts in the field of computing and communication, including full-length papers, research-in-progress papers, and case studies related to all the areas of data mining, machine learning, Internet of Things (IoT), and information security.
oW should coded communication be approached? Is it about prob H ability theorems and bounds, or about algorithms and structures? The traditional course in information theory and coding teaches these together in one course in which the Shannon theory, a probabilistic the ory of information, dominates. The theory's predictions and bounds to performance are valuable to the coding engineer, but coding today is mostly about structures and algorithms and their size, speed and error performance. While coding has a theoretical basis, it has a practical side as well, an engineering side in which costs and benefits matter. It is safe to say that most of the recent advances in information theory and coding are in the engineering of coding. These thoughts motivate the present text book: A coded communication book based on methods and algorithms, with information theory in a necessary but supporting role. There has been muchrecent progress in coding, both inthe theory and the practice, and these pages report many new advances. Chapter 2 cov ers traditional source coding, but also the coding ofreal one-dimensional sources like speech and new techniques like vector quantization. Chapter 4 is a unified treatment of trellis codes, beginning with binary convolu tional codes and passing to the new trellis modulation codes.
