Guessing Random Additive Noise Decoding [electronic resource] : A Hardware Perspective / by Syed Mohsin Abbas, Marwan Jalaleddine, Warren J. Gross.

Por: Abbas, Syed Mohsin [author.]Colaborador(es): Jalaleddine, Marwan [author.] | Gross, Warren J [author.] | SpringerLink (Online service)Tipo de material: TextoTextoEditor: Cham : Springer Nature Switzerland : Imprint: Springer, 2023Edición: 1st ed. 2023Descripción: XIV, 151 p. 114 illus., 101 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783031316630Tema(s): Coding theory | Information theory | Telecommunication | Logic design | Computer arithmetic and logic units | Coding and Information Theory | Communications Engineering, Networks | Logic Design | Arithmetic and Logic StructuresFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 003.54 Clasificación LoC:QA268Q350-390Recursos en línea: Libro electrónicoTexto
Contenidos:
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.
En: Springer Nature eBookResumen: 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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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.

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