Iterative signal detection for large scale GSM-MIMO systems

Detalhes bibliográficos
Autor(a) principal: Lopes, H.
Data de Publicação: 2018
Outros Autores: Souto, N.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10071/16158
Resumo: Generalized spatial modulations (GSM) represent a novel multiple input multiple output (MIMO) scheme which can be regarded as a compromise between spatial multiplexing MIMO and conventional spatial modulations (SM), achieving both spectral efficiency (SE) and energy efficiency (EE). Due to the high computational complexity of the maximum likelihood detector (MLD) in large antenna settings and symbol constellations, in this paper we propose a lower complexity iterative suboptimal detector. The derived algorithm comprises a sequence of simple processing steps, namely an unconstrained Euclidean distance minimization problem, an element wise projection over the signal constellation and a projection over the set of valid active antenna combinations. To deal with scenarios where the number of possible active antenna combinations is large, an alternative version of the algorithm which adopts a simpler cardinality projection is also presented. Simulation results show that, compared with other existing approaches, both versions of the proposed algorithm are effective in challenging underdetermined scenarios where the number of receiver antennas is lower than the number of transmitter antennas.
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spelling Iterative signal detection for large scale GSM-MIMO systemsGeneralized Spatial Modulations (GSM)Large Scale MIMO (LS-MIMO)Compressed Sensing (CS)Generalized spatial modulations (GSM) represent a novel multiple input multiple output (MIMO) scheme which can be regarded as a compromise between spatial multiplexing MIMO and conventional spatial modulations (SM), achieving both spectral efficiency (SE) and energy efficiency (EE). Due to the high computational complexity of the maximum likelihood detector (MLD) in large antenna settings and symbol constellations, in this paper we propose a lower complexity iterative suboptimal detector. The derived algorithm comprises a sequence of simple processing steps, namely an unconstrained Euclidean distance minimization problem, an element wise projection over the signal constellation and a projection over the set of valid active antenna combinations. To deal with scenarios where the number of possible active antenna combinations is large, an alternative version of the algorithm which adopts a simpler cardinality projection is also presented. Simulation results show that, compared with other existing approaches, both versions of the proposed algorithm are effective in challenging underdetermined scenarios where the number of receiver antennas is lower than the number of transmitter antennas.IEEE2018-06-18T15:26:52Z2018-01-01T00:00:00Z20182018-12-11T14:30:36Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/16158eng0018-954510.1109/TVT.2018.2820201Lopes, H.Souto, N.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:46:10Zoai:repositorio.iscte-iul.pt:10071/16158Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:22:10.139529Repositório 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 Iterative signal detection for large scale GSM-MIMO systems
title Iterative signal detection for large scale GSM-MIMO systems
spellingShingle Iterative signal detection for large scale GSM-MIMO systems
Lopes, H.
Generalized Spatial Modulations (GSM)
Large Scale MIMO (LS-MIMO)
Compressed Sensing (CS)
title_short Iterative signal detection for large scale GSM-MIMO systems
title_full Iterative signal detection for large scale GSM-MIMO systems
title_fullStr Iterative signal detection for large scale GSM-MIMO systems
title_full_unstemmed Iterative signal detection for large scale GSM-MIMO systems
title_sort Iterative signal detection for large scale GSM-MIMO systems
author Lopes, H.
author_facet Lopes, H.
Souto, N.
author_role author
author2 Souto, N.
author2_role author
dc.contributor.author.fl_str_mv Lopes, H.
Souto, N.
dc.subject.por.fl_str_mv Generalized Spatial Modulations (GSM)
Large Scale MIMO (LS-MIMO)
Compressed Sensing (CS)
topic Generalized Spatial Modulations (GSM)
Large Scale MIMO (LS-MIMO)
Compressed Sensing (CS)
description Generalized spatial modulations (GSM) represent a novel multiple input multiple output (MIMO) scheme which can be regarded as a compromise between spatial multiplexing MIMO and conventional spatial modulations (SM), achieving both spectral efficiency (SE) and energy efficiency (EE). Due to the high computational complexity of the maximum likelihood detector (MLD) in large antenna settings and symbol constellations, in this paper we propose a lower complexity iterative suboptimal detector. The derived algorithm comprises a sequence of simple processing steps, namely an unconstrained Euclidean distance minimization problem, an element wise projection over the signal constellation and a projection over the set of valid active antenna combinations. To deal with scenarios where the number of possible active antenna combinations is large, an alternative version of the algorithm which adopts a simpler cardinality projection is also presented. Simulation results show that, compared with other existing approaches, both versions of the proposed algorithm are effective in challenging underdetermined scenarios where the number of receiver antennas is lower than the number of transmitter antennas.
publishDate 2018
dc.date.none.fl_str_mv 2018-06-18T15:26:52Z
2018-01-01T00:00:00Z
2018
2018-12-11T14:30:36Z
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/10071/16158
url http://hdl.handle.net/10071/16158
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0018-9545
10.1109/TVT.2018.2820201
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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
institution RCAAP
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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