Guessing Random Additive Noise Decoding (eBook)

A Hardware Perspective
eBook Download: PDF
2023 | 2023
XIV, 151 Seiten
Springer Nature Switzerland (Verlag)
978-3-031-31663-0 (ISBN)

Lese- und Medienproben

Guessing Random Additive Noise Decoding - Syed Mohsin Abbas, Marwan Jalaleddine, Warren J. Gross
Systemvoraussetzungen
149,79 inkl. MwSt
  • Download sofort lieferbar
  • Zahlungsarten anzeigen
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.



Syed Mohsin Abbas is a postdoctoral researcher at Integrated Systems for Information Processing (ISIP) Lab at McGill University, Canada. He received his PhD. from the Department of Electronics and Computer Engineering (ECE) at the Hong Kong University of Science and Technology (HKUST). Dr. Abbas's research interests include the development of high-throughput and energy-efficient VLSI architectures for modern channel code decoders. In addition, his curiosity is fueled by topics such as information theory, VLSI Design, Computer Architecture, Embedded Systems, Massive MIMO, and 5G/6G communication.

Marwan Jalaleddine is a PhD. candidate and teaching assistant at McGill University. His research interests lie in modern Error Correcting Codes (ECCs) and their application in wireless communication technology.

Warren J. Gross is a James McGill Professor and the Chair of the Department of Electrical and Computer Engineering at McGill University, Montreal, QC, Canada. He received the PhD degree from the University of Toronto. His research interests are in the design and implementation of signal processing systems and custom computer architectures. He served as the Chair of the IEEE Signal Processing Society Technical Committee on Design and Implementation of Signal Processing Systems. He has served as a General Chair and Technical Program Chair of several conferences and workshops. He served as an Associate Editor for the IEEE Transactions on Signal Processing and as a Senior Area Editor.


Erscheint lt. Verlag 17.8.2023
Zusatzinfo XIV, 151 p. 114 illus., 101 illus. in color.
Sprache englisch
Themenwelt Mathematik / Informatik Informatik
Technik Nachrichtentechnik
Schlagworte Area-Efficiency • Grand • High-Throughput • low-latency • Maximum Likelihood Decoder • URLLC • VLSI Architecture
ISBN-10 3-031-31663-0 / 3031316630
ISBN-13 978-3-031-31663-0 / 9783031316630
Haben Sie eine Frage zum Produkt?
PDFPDF (Wasserzeichen)
Größe: 15,6 MB

DRM: Digitales Wasserzeichen
Dieses eBook enthält ein digitales Wasser­zeichen und ist damit für Sie persona­lisiert. Bei einer missbräuch­lichen Weiter­gabe des eBooks an Dritte ist eine Rück­ver­folgung an die Quelle möglich.

Dateiformat: PDF (Portable Document Format)
Mit einem festen Seiten­layout eignet sich die PDF besonders für Fach­bücher mit Spalten, Tabellen und Abbild­ungen. Eine PDF kann auf fast allen Geräten ange­zeigt werden, ist aber für kleine Displays (Smart­phone, eReader) nur einge­schränkt geeignet.

Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen dafür einen PDF-Viewer - z.B. den Adobe Reader oder Adobe Digital Editions.
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen dafür einen PDF-Viewer - z.B. die kostenlose Adobe Digital Editions-App.

Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.

Mehr entdecken
aus dem Bereich
Konzepte, Methoden, Lösungen und Arbeitshilfen für die Praxis

von Ernst Tiemeyer

eBook Download (2023)
Carl Hanser Verlag GmbH & Co. KG
69,99
Konzepte, Methoden, Lösungen und Arbeitshilfen für die Praxis

von Ernst Tiemeyer

eBook Download (2023)
Carl Hanser Verlag GmbH & Co. KG
69,99
Der Weg zur professionellen Vektorgrafik

von Uwe Schöler

eBook Download (2024)
Carl Hanser Verlag GmbH & Co. KG
29,99