Neural network channel estimator for time-variant frequency-selective fading channels

Página do item simplificado
dc.contributor.authorBarragam V.P.
dc.contributor.authorJerji F.
dc.contributor.authorAkamine C.
dc.date.accessioned2024-03-12T19:08:16Z
dc.date.available2024-03-12T19:08:16Z
dc.date.issued2023
dc.description.abstract© 2023 The Authors. Electronics Letters published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.The next generations of wireless communications systems are pushing the limits of the channel estimation methods utilized in the orthogonal frequency division multiplexing receptors. This letter proposes a novel channel estimation method using a densely connected neural network considering the time-variant frequency-selective fading channel model. A fully connected deep neural network for the AWGN channel case is also proposed. The comparative complexity of the estimation for different channel models is also discussed. The simulation results demonstrate that the densely connected neural network method surpasses the minimum mean-square error method performance for a signal-to-noise ratio ranging from 0 to 25 dB in the frequency-selective channel.
dc.description.issuenumber22
dc.description.volume59
dc.identifier.doi10.1049/ell2.13022
dc.identifier.issn0013-5194
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/34007
dc.relation.ispartofElectronics Letters
dc.rightsAcesso Aberto
dc.subject.otherlanguageartificial intelligence
dc.subject.otherlanguageAWGN channels
dc.subject.otherlanguagebackpropagation
dc.subject.otherlanguagechannel estimation
dc.subject.otherlanguageconvolutional neural nets
dc.subject.otherlanguagefading channels
dc.subject.otherlanguageneural nets
dc.subject.otherlanguageorthogonal frequency division multiplexing modulation
dc.subject.otherlanguageRayleigh channels
dc.subject.otherlanguagewireless communications
dc.titleNeural network channel estimator for time-variant frequency-selective fading channels
dc.typeArtigo
local.scopus.citations1
local.scopus.eid2-s2.0-85176380984
local.scopus.subjectAWGN channel
local.scopus.subjectConvolutional neural net
local.scopus.subjectFadings channels
local.scopus.subjectFrequency-selective fading channels
local.scopus.subjectNeural-networks
local.scopus.subjectOrthogonal frequency division multiplexing modulation
local.scopus.subjectOrthogonal frequency-division multiplexing
local.scopus.subjectRayleigh channel
local.scopus.subjectTime variant
local.scopus.subjectWireless communications
local.scopus.updated2025-04-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85176380984&origin=inward
Arquivos
Coleções
Artigos de periódico