000 04512nam a22006135i 4500
001 978-3-031-31663-0
003 DE-He213
005 20240423125544.0
007 cr nn 008mamaa
008 230817s2023 sz | s |||| 0|eng d
020 _a9783031316630
_9978-3-031-31663-0
024 7 _a10.1007/978-3-031-31663-0
_2doi
050 4 _aQA268
050 4 _aQ350-390
072 7 _aGPJ
_2bicssc
072 7 _aGPF
_2bicssc
072 7 _aCOM031000
_2bisacsh
072 7 _aGPJ
_2thema
072 7 _aGPF
_2thema
082 0 4 _a003.54
_223
100 1 _aAbbas, Syed Mohsin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aGuessing Random Additive Noise Decoding
_h[electronic resource] :
_bA Hardware Perspective /
_cby Syed Mohsin Abbas, Marwan Jalaleddine, Warren J. Gross.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2023.
300 _aXIV, 151 p. 114 illus., 101 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _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.
520 _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.
650 0 _aCoding theory.
650 0 _aInformation theory.
650 0 _aTelecommunication.
650 0 _aLogic design.
650 0 _aComputer arithmetic and logic units.
650 1 4 _aCoding and Information Theory.
650 2 4 _aCommunications Engineering, Networks.
650 2 4 _aLogic Design.
650 2 4 _aArithmetic and Logic Structures.
700 1 _aJalaleddine, Marwan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aGross, Warren J.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031316623
776 0 8 _iPrinted edition:
_z9783031316647
776 0 8 _iPrinted edition:
_z9783031316654
856 4 0 _uhttps://doi.org/10.1007/978-3-031-31663-0
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c179240
_d179240