Download or read book Guessing Random Additive Noise Decoding written by Syed Mohsin Abbas and published by Springer Nature. This book was released on 2023-08-17 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives a detailed overview of a universal Maximum Likelihood (ML) decoding technique, known as Guessing Random Additive Noise Decoding (GRAND), has been introduced for short-length and high-rate linear block codes. The interest in short channel codes and the corresponding ML decoding algorithms has recently been reignited in both industry and academia due to emergence of applications with strict reliability and ultra-low latency requirements . A few of these applications include Machine-to-Machine (M2M) communication, augmented and virtual Reality, Intelligent Transportation Systems (ITS), the Internet of Things (IoTs), and Ultra-Reliable and Low Latency Communications (URLLC), which is an important use case for the 5G-NR standard. GRAND features both soft-input and hard-input variants. Moreover, there are traditional GRAND variants that can be used with any communication channel, and specialized GRAND variants that are developed for a specific communication channel. This book presents a detailed overview of these GRAND variants and their hardware architectures. The book is structured into four parts. Part 1 introduces linear block codes and the GRAND algorithm. Part 2 discusses the hardware architecture for traditional GRAND variants that can be applied to any underlying communication channel. Part 3 describes the hardware architectures for specialized GRAND variants developed for specific communication channels. Lastly, Part 4 provides an overview of recently proposed GRAND variants and their unique applications. This book is ideal for researchers or engineers looking to implement high-throughput and energy-efficient hardware for GRAND, as well as seasoned academics and graduate students interested in the topic of VLSI hardware architectures. Additionally, it can serve as reading material in graduate courses covering modern error correcting codes and Maximum Likelihood decoding for short codes.
Download or read book Quantized Guessing Random Additive Noise Decoding written by Evan Gabhart and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Guessing Random Additive Noise Decoding (GRAND) has proven to be a universal, maximum likelihood decoder. Multiple extensions of GRAND have been introduced, giving way to a class of universal decoders. GRAND itself describes a hard-detection decoder, so a natural extension was to incorporate the use of soft-information. The result was Soft Guessing Random Additive Noise Decoding (SGRAND). SGRAND assumes access to complete soft information, proving itself to be a maximum-likelihood soft-detection decoder. Physical limitations, however, prevent one from having access to perfect soft-information in practice. This thesis proposes an approximation to the optimal performance of SGRAND, Quantized Guessing Random Additive Noise Decoding (QGRAND). I describe the algorithm and evaluate its performance compared to hard-detection GRAND, SGRAND, and another approach to approximating SGRAND, Ordered Reliability Bits GRAND (ORBGRAND). QGRAND also allows itself to be tailored to an arbitrary number of bits of soft information, and I will show as the number of bits increases so does performance. I then use the GRAND algorithms discussed in order to evaluate error correction potential of different channel codes, particularly Polar Adjusted Convolutional codes, CA-Polar codes, and CRCs.
Download or read book Guessing Random Additive Noise Decoding GRAND from Performance to Implementation written by Wei An (Scientist in electrical engineering) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Armed with both hard and soft detection variants of GRAND, Cyclic Redundancy Check (CRC) codes are evaluated and recognized with excellent performance, beating state-of-art CA-Polar codes. Random Linear Codes (RLCs) are also enabled to be good candidates for their security features. Owing to the advent of GRAND, the two codes, having long been neglected for error correction, become good candidates to URLLC applications, as presented in Chapter 4.
Download or read book Trellises and Trellis Based Decoding Algorithms for Linear Block Codes written by Shu Lin and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the demand for data reliability increases, coding for error control becomes increasingly important in data transmission systems and has become an integral part of almost all data communication system designs. In recent years, various trellis-based soft-decoding algorithms for linear block codes have been devised. New ideas developed in the study of trellis structure of block codes can be used for improving decoding and analyzing the trellis complexity of convolutional codes. These recent developments provide practicing communication engineers with more choices when designing error control systems. Trellises and Trellis-based Decoding Algorithms for Linear Block Codes combines trellises and trellis-based decoding algorithms for linear codes together in a simple and unified form. The approach is to explain the material in an easily understood manner with minimal mathematical rigor. Trellises and Trellis-based Decoding Algorithms for Linear Block Codes is intended for practicing communication engineers who want to have a fast grasp and understanding of the subject. Only material considered essential and useful for practical applications is included. This book can also be used as a text for advanced courses on the subject.
Download or read book Information Theory Inference and Learning Algorithms written by David J. C. MacKay and published by Cambridge University Press. This book was released on 2003-09-25 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.
Download or read book Coding Theorems of Information Theory written by Jacob Wolfowitz and published by Springer. This book was released on 2013-04-17 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph originated with a course of lectures on information theory which I gave at Cornell University during the academic year 1958-1959. It has no pretensions to exhaustiveness, and, indeed, no pretensions at all. Its purpose is to provide, for mathematicians of some maturity, an easy introduction to the ideas and principal known theorems of a certain body of coding theory. This purpose will be amply achieved if the reader is enabled, through his reading, to read the (sometimes obscurely written) literature and to obtain results of his own. The theory is ob viously in a rapid stage of development; even while this monograph was in manuscript several of its readers obtained important new results. The first chapter is introductory and the subject matter of the monograph is described at the end of the chapter. There does not seem to be a uniquely determined logical order in which the material should be arranged. In determining the final arrangement I tried to obtain an order which makes reading easy and yet is not illogical. I can only hope that the resultant compromises do not earn me the criticism that I failed on both counts. There are a very few instances in the monograph where a stated theorem is proved by a method which is based on a result proved only later.
Download or read book Information Theory and Reliable Communication written by Robert Gallager and published by Springer. This book was released on 2014-05-04 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Towards Achieving Ultra Reliable Low Latency Communications Using Guessing Random Additive Noise Decoding written by Marwan Jalaleddine and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Ultra-reliable and low latency communications (URLLCs) is one of the key pillars of the 5G communications standard which is used to enable applications ranging from the smart grid to robot control. In the upcoming communication standards, more stringent requirements are being established on the end-to-end latency and reliability of data.In an effort to build upon the current advancements in URLLC and guessing random additive noise decoding (GRAND), we develop the partitioned GRAND (PGRAND) which uses the quantized reliability information from the channel to generate the most likely test error patterns. We assess the performance of PGRAND on 5G NR CA-polar code, random linear code, and cyclic redundancy check codes. PGRAND provides superior performance to that of ordered reliability bit GRAND at high signal-to-noise ratios (SNRs) by achieving a 0.2dB gain at a frame error rate (FER) of 10^(-4) and a 50% reduction in the average queries per frame performance at Eb/N0 > 5.5dB. Additionally, PGRAND approaches the FER performance of soft maximum likelihood GRAND at high SNRs with less scheduling complexity. This makes PGRAND a desirable candidate as a near maximum likelihood code agnostic decoder for any short, high rate code. Alternatively, we also develop guessing random additive noise-assisted decoding (AGRAND) that can be used alongside any conventional decoder to improve the decoder latency. If AGRAND succeeds to find a version of the codeword that belongs to the codebook, the decoder terminates early, saving latency and power. This decoding scheme can reduce latency by up to 84% at Eb/N0 = 5.5dB when used with successive cancellation list decoding on CA-polar code. As such, AGRAND enables maximum likelihood low latency decoding of CA-polar codes"--
Download or read book Information Physics and Computation written by Marc Mézard and published by Oxford University Press. This book was released on 2009-01-22 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt: A very active field of research is emerging at the frontier of statistical physics, theoretical computer science/discrete mathematics, and coding/information theory. This book sets up a common language and pool of concepts, accessible to students and researchers from each of these fields.
Download or read book Elements of Information Theory written by Thomas M. Cover and published by John Wiley & Sons. This book was released on 2012-11-28 with total page 788 pages. Available in PDF, EPUB and Kindle. Book excerpt: The latest edition of this classic is updated with new problem sets and material The Second Edition of this fundamental textbook maintains the book's tradition of clear, thought-provoking instruction. Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory. All the essential topics in information theory are covered in detail, including entropy, data compression, channel capacity, rate distortion, network information theory, and hypothesis testing. The authors provide readers with a solid understanding of the underlying theory and applications. Problem sets and a telegraphic summary at the end of each chapter further assist readers. The historical notes that follow each chapter recap the main points. The Second Edition features: * Chapters reorganized to improve teaching * 200 new problems * New material on source coding, portfolio theory, and feedback capacity * Updated references Now current and enhanced, the Second Edition of Elements of Information Theory remains the ideal textbook for upper-level undergraduate and graduate courses in electrical engineering, statistics, and telecommunications.
Download or read book Computational Complexity written by Sanjeev Arora and published by Cambridge University Press. This book was released on 2009-04-20 with total page 609 pages. Available in PDF, EPUB and Kindle. Book excerpt: New and classical results in computational complexity, including interactive proofs, PCP, derandomization, and quantum computation. Ideal for graduate students.
Download or read book Geometric Programming for Communication Systems written by Mung Chiang and published by Now Publishers Inc. This book was released on 2005 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently Geometric Programming has been applied to study a variety of problems in the analysis and design of communication systems from information theory and queuing theory to signal processing and network protocols. Geometric Programming for Communication Systems begins its comprehensive treatment of the subject by providing an in-depth tutorial on the theory, algorithms, and modeling methods of Geometric Programming. It then gives a systematic survey of the applications of Geometric Programming to the study of communication systems. It collects in one place various published results in this area, which are currently scattered in several books and many research papers, as well as to date unpublished results. Geometric Programming for Communication Systems is intended for researchers and students who wish to have a comprehensive starting point for understanding the theory and applications of geometric programming in communication systems.
Download or read book List Decoding of Error Correcting Codes written by Venkatesan Guruswami and published by Springer Science & Business Media. This book was released on 2004-11-29 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph is a thoroughly revised and extended version of the author's PhD thesis, which was selected as the winning thesis of the 2002 ACM Doctoral Dissertation Competition. Venkatesan Guruswami did his PhD work at the MIT with Madhu Sudan as thesis adviser. Starting with the seminal work of Shannon and Hamming, coding theory has generated a rich theory of error-correcting codes. This theory has traditionally gone hand in hand with the algorithmic theory of decoding that tackles the problem of recovering from the transmission errors efficiently. This book presents some spectacular new results in the area of decoding algorithms for error-correcting codes. Specificially, it shows how the notion of list-decoding can be applied to recover from far more errors, for a wide variety of error-correcting codes, than achievable before The style of the exposition is crisp and the enormous amount of information on combinatorial results, polynomial time list decoding algorithms, and applications is presented in well structured form.
Download or read book The Algorithmic Foundations of Differential Privacy written by Cynthia Dwork and published by . This book was released on 2014 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition. The Algorithmic Foundations of Differential Privacy starts out by motivating and discussing the meaning of differential privacy, and proceeds to explore the fundamental techniques for achieving differential privacy, and the application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some powerful computational results, there are still fundamental limitations. Virtually all the algorithms discussed herein maintain differential privacy against adversaries of arbitrary computational power -- certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed. The monograph then turns from fundamentals to applications other than query-release, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams, is discussed. The Algorithmic Foundations of Differential Privacy is meant as a thorough introduction to the problems and techniques of differential privacy, and is an invaluable reference for anyone with an interest in the topic.
Download or read book Noise centric Decoding written by Amit Solomon and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Maximum Likelihood (ML) decoding of forward error correction codes is known to be optimally accurate, but is not used in practice due to the lack of a feasible implementation. As the common approach in coding theory is a code-centric one, designing a ML decoder is a challenging code-specific task. We establish a noise-centric approach for decoding of error correction codes that enables us to introduce a universal ML soft detection decoder called Soft Guessing Random Additive Noise Decoder (SGRAND), which is a development of a previously described hard detection ML decoder called Guessing Random Additive Noise Decoder (GRAND), that fully avails of soft detection information. SGRAND is suitable for use with any arbitrary moderate redundancy block code. A further development of the algorithm is provided that can decode coded signals transmitted on Multiple Access Channels (MACs), where transmitters not only suffer from noise, but also interfere one another. We propose a scheme that deals with the two problems of MAC separately: interference and the noise. We prove that a scheme based on SGRAND results in optimally accurate decodings. Finally, we study how correlated noise between orthogonal channels can be used to improve rates and reduce Block Error Rate (BLER) performance via a scheme called Noise Recycling.
Download or read book Foundations of Data Science written by Avrim Blum and published by Cambridge University Press. This book was released on 2020-01-23 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
Download or read book Software Defined Radio for Engineers written by Alexander M. Wyglinski and published by Artech House. This book was released on 2018-04-30 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on the popular Artech House classic, Digital Communication Systems Engineering with Software-Defined Radio, this book provides a practical approach to quickly learning the software-defined radio (SDR) concepts needed for work in the field. This up-to-date volume guides readers on how to quickly prototype wireless designs using SDR for real-world testing and experimentation. This book explores advanced wireless communication techniques such as OFDM, LTE, WLA, and hardware targeting. Readers will gain an understanding of the core concepts behind wireless hardware, such as the radio frequency front-end, analog-to-digital and digital-to-analog converters, as well as various processing technologies. Moreover, this volume includes chapters on timing estimation, matched filtering, frame synchronization message decoding, and source coding. The orthogonal frequency division multiplexing is explained and details about HDL code generation and deployment are provided. The book concludes with coverage of the WLAN toolbox with OFDM beacon reception and the LTE toolbox with downlink reception. Multiple case studies are provided throughout the book. Both MATLAB and Simulink source code are included to assist readers with their projects in the field.
