Symbol-level GRAND for high-order modulation over block fading channels

Detalhes bibliográficos
Autor(a) principal: Chatzigeorgiou, I.
Data de Publicação: 2023
Outros Autores: Monteiro, F. A.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10071/28138
Resumo: Guessing random additive noise decoding (GRAND) is a noise-centric decoding method, which is suitable for low-latency communications, as it supports error correction codes that generate short codewords. GRAND estimates transmitted codewords by guessing the error patterns that altered them during transmission. The guessing process requires the testing of error patterns that are arranged in increasing order of Hamming weight. This approach is fitting for binary transmission over additive white Gaussian noise channels. This letter considers transmission of coded and modulated data over block fading channels and proposes a more computationally efficient variant of GRAND, which leverages information on the modulation scheme and the fading channel. In the core of the proposed variant, referred to as symbol-level GRAND, is an expression that approximately computes the probability of occurrence of an error pattern and determines the order with which error patterns are tested. Analysis and simulation results demonstrate that symbol-level GRAND produces estimates of the transmitted codewords faster than the original GRAND at the cost of a small increase in memory requirements.
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spelling Symbol-level GRAND for high-order modulation over block fading channelsRandom linear codesGRANDHard detectionBlock fadingQAMShort-packet communicationURLLCGuessing random additive noise decoding (GRAND) is a noise-centric decoding method, which is suitable for low-latency communications, as it supports error correction codes that generate short codewords. GRAND estimates transmitted codewords by guessing the error patterns that altered them during transmission. The guessing process requires the testing of error patterns that are arranged in increasing order of Hamming weight. This approach is fitting for binary transmission over additive white Gaussian noise channels. This letter considers transmission of coded and modulated data over block fading channels and proposes a more computationally efficient variant of GRAND, which leverages information on the modulation scheme and the fading channel. In the core of the proposed variant, referred to as symbol-level GRAND, is an expression that approximately computes the probability of occurrence of an error pattern and determines the order with which error patterns are tested. Analysis and simulation results demonstrate that symbol-level GRAND produces estimates of the transmitted codewords faster than the original GRAND at the cost of a small increase in memory requirements.IEEE2024-12-07T00:00:00Z2023-01-01T00:00:00Z20232023-03-02T15:15:22Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/28138eng1089-779810.1109/LCOMM.2022.3227593Chatzigeorgiou, I.Monteiro, F. A.info:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:46:51Zoai:repositorio.iscte-iul.pt:10071/28138Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:22:38.425691Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Symbol-level GRAND for high-order modulation over block fading channels
title Symbol-level GRAND for high-order modulation over block fading channels
spellingShingle Symbol-level GRAND for high-order modulation over block fading channels
Chatzigeorgiou, I.
Random linear codes
GRAND
Hard detection
Block fading
QAM
Short-packet communication
URLLC
title_short Symbol-level GRAND for high-order modulation over block fading channels
title_full Symbol-level GRAND for high-order modulation over block fading channels
title_fullStr Symbol-level GRAND for high-order modulation over block fading channels
title_full_unstemmed Symbol-level GRAND for high-order modulation over block fading channels
title_sort Symbol-level GRAND for high-order modulation over block fading channels
author Chatzigeorgiou, I.
author_facet Chatzigeorgiou, I.
Monteiro, F. A.
author_role author
author2 Monteiro, F. A.
author2_role author
dc.contributor.author.fl_str_mv Chatzigeorgiou, I.
Monteiro, F. A.
dc.subject.por.fl_str_mv Random linear codes
GRAND
Hard detection
Block fading
QAM
Short-packet communication
URLLC
topic Random linear codes
GRAND
Hard detection
Block fading
QAM
Short-packet communication
URLLC
description Guessing random additive noise decoding (GRAND) is a noise-centric decoding method, which is suitable for low-latency communications, as it supports error correction codes that generate short codewords. GRAND estimates transmitted codewords by guessing the error patterns that altered them during transmission. The guessing process requires the testing of error patterns that are arranged in increasing order of Hamming weight. This approach is fitting for binary transmission over additive white Gaussian noise channels. This letter considers transmission of coded and modulated data over block fading channels and proposes a more computationally efficient variant of GRAND, which leverages information on the modulation scheme and the fading channel. In the core of the proposed variant, referred to as symbol-level GRAND, is an expression that approximately computes the probability of occurrence of an error pattern and determines the order with which error patterns are tested. Analysis and simulation results demonstrate that symbol-level GRAND produces estimates of the transmitted codewords faster than the original GRAND at the cost of a small increase in memory requirements.
publishDate 2023
dc.date.none.fl_str_mv 2023-01-01T00:00:00Z
2023
2023-03-02T15:15:22Z
2024-12-07T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10071/28138
url http://hdl.handle.net/10071/28138
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1089-7798
10.1109/LCOMM.2022.3227593
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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