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Guessing random additive noise decodinga hardware perspective /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Guessing random additive noise decodingby Syed Mohsin Abbas, Marwan Jalaleddine, Warren J. Gross.
Reminder of title:	a hardware perspective /
Author:	Abbas, Syed Mohsin.
other author:	Jalaleddine, Marwan.
Published:	Cham :Springer Nature Switzerland :2023.
Description:	xiv, 151 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Decoders (Electronics)
Online resource:	https://doi.org/10.1007/978-3-031-31663-0
ISBN:	9783031316630$q(electronic bk.)

Guessing random additive noise decodinga hardware perspective /
Abbas, Syed Mohsin.

Guessing random additive noise decodinga hardware perspective /[electronic resource] :by Syed Mohsin Abbas, Marwan Jalaleddine, Warren J. Gross. - Cham :Springer Nature Switzerland :2023. - xiv, 151 p. :ill. (some col.), digital ;24 cm.

Guessing Random Additive Noise Decoding (GRAND) -- Hardware Architecture for GRAND with ABandonment (GRANDAB) -- Hardware Architecture for Ordered Reliability Bits GRAND (ORBGRAND) -- Hardware Architecture for List GRAND (LGRAND) -- Hardware Architecture for GRAND Markov Order (GRAND-MO) -- Hardware Architecture for Fading-GRAND -- A survey of recent GRAND variants.

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.

ISBN: 9783031316630$q(electronic bk.)

Standard No.: 10.1007/978-3-031-31663-0doiSubjects--Topical Terms:

711274
Decoders (Electronics)

LC Class. No.: TK7872.D37

Dewey Class. No.: 621.3822

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520 $a 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.
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