LEADER 04328nam 22006495 450 001 9910741176203321 005 20230817125338.0 010 $a3-031-31663-0 024 7 $a10.1007/978-3-031-31663-0 035 $a(MiAaPQ)EBC30706867 035 $a(CKB)27994406300041 035 $a(Au-PeEL)EBL30706867 035 $a(DE-He213)978-3-031-31663-0 035 $a(PPN)272272663 035 $a(EXLCZ)9927994406300041 100 $a20230817d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGuessing Random Additive Noise Decoding $eA Hardware Perspective /$fby Syed Mohsin Abbas, Marwan Jalaleddine, Warren J. Gross 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (157 pages) 311 $a9783031316623 327 $aGuessing 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. 330 $aThis 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. 606 $aCoding theory 606 $aInformation theory 606 $aTelecommunication 606 $aLogic design 606 $aComputer arithmetic and logic units 606 $aCoding and Information Theory 606 $aCommunications Engineering, Networks 606 $aLogic Design 606 $aArithmetic and Logic Structures 615 0$aCoding theory. 615 0$aInformation theory. 615 0$aTelecommunication. 615 0$aLogic design. 615 0$aComputer arithmetic and logic units. 615 14$aCoding and Information Theory. 615 24$aCommunications Engineering, Networks. 615 24$aLogic Design. 615 24$aArithmetic and Logic Structures. 676 $a621.3822 700 $aAbbas$b Syed Mohsin$01423758 701 $aJalaleddine$b Marwan$01423759 701 $aGross$b Warren J$01423760 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910741176203321 996 $aGuessing Random Additive Noise Decoding$93552192 997 $aUNINA