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Author: Syed Mohsin Abbas
Publisher: Springer Nature
ISBN: 3031316630
Category : Computers
Languages : en
Pages : 157
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Book Description
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.
Author: Syed Mohsin Abbas
Publisher: Springer Nature
ISBN: 3031316630
Category : Computers
Languages : en
Pages : 157
Get Book Here
Book Description
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.
Author: Evan Gabhart
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
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.
Author: Wei An (Scientist in electrical engineering)
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
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.
Author: Shu Lin
Publisher: Springer Science & Business Media
ISBN: 1461557453
Category : Technology & Engineering
Languages : en
Pages : 290
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Book Description
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.
Author: David J. C. MacKay
Publisher: Cambridge University Press
ISBN: 9780521642989
Category : Computers
Languages : en
Pages : 694
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Book Description
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.
Author: Jacob Wolfowitz
Publisher: Springer Science & Business Media
ISBN: 366200237X
Category : Computers
Languages : en
Pages : 165
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Book Description
The imminent exhaustion of the first printing of this monograph and the kind willingness of the publishers have presented me with the opportunity to correct a few minor misprints and to make a number of additions to the first edition. Some of these additions are in the form of remarks scattered throughout the monograph. The principal additions are Chapter 11, most of Section 6. 6 (inc1uding Theorem 6. 6. 2), Sections 6. 7, 7. 7, and 4. 9. It has been impossible to inc1ude all the novel and inter esting results which have appeared in the last three years. I hope to inc1ude these in a new edition or a new monograph, to be written in a few years when the main new currents of research are more clearly visible. There are now several instances where, in the first edition, only a weak converse was proved, and, in the present edition, the proof of a strong converse is given. Where the proof of the weaker theorem em ploys a method of general application and interest it has been retained and is given along with the proof of the stronger result. This is wholly in accord with the purpose of the present monograph, which is not only to prove the principal coding theorems but also, while doing so, to acquaint the reader with the most fruitful and interesting ideas and methods used in the theory. I am indebted to Dr.
Author: Robert Gallager
Publisher: Springer
ISBN: 3709129451
Category : Technology & Engineering
Languages : en
Pages : 116
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Book Description
Author: Marwan Jalaleddine
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
"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"--
Author: Thomas M. Cover
Publisher: John Wiley & Sons
ISBN: 1118585771
Category : Computers
Languages : en
Pages : 788
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Book Description
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.
Author: Marc Mézard
Publisher: Oxford University Press
ISBN: 019857083X
Category : Computers
Languages : en
Pages : 584
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Book Description
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.
