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Analysis of the Fifth Generation NOMA System Using LSTMAlgorithm

cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.author-orcid0000-0003-3721-6473
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
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cris.virtualsource.author-orcid3fe42726-36c4-478a-818f-a10f72d4a6ef
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
dc.abstract.enThis study investigates non-orthogonal multiple access (NOMA) receivers based on deep learning (DL) employing the long-short term memory technique (LSTM) over frequency-flat Rayleigh distributed fading links. The fading links are independently and identically distributed (i.i.d.). When comparing the DL-based NOMA receiver’s system performance to that of the traditional NOMA technique, the DL-based receiver surpasses the conventional successive interference cancellation (SIC)-based NOMA receiver. The simulations are conducted for various values of cyclic prefix (CP) considering the clipping noise (CN) under real-time propagation characteristics. It has been discovered that neither minimum mean square error (MMSE) nor least square error (LSE) can provide precise information on fading channel coe ffi cients. With a signal-to-noise ratio (SNR) value exceeding 14 dB, precision tends to be saturated. On the other hand, DL techniques continue to be e ff ective in channel estimation and detection. Lower learning rates improve system performance, whereas a high learning rate generates rapid changes in the weights of the DL NOMA detector, leading to a very high validation error value.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Inżynierii Biosystemów
dc.contributor.authorBhatt, Abhishek
dc.contributor.authorShankar, Ravi
dc.contributor.authorNiedbała, Gniewko
dc.contributor.authorRupani, Ajay
dc.date.access2026-01-09
dc.date.accessioned2026-01-09T19:32:17Z
dc.date.available2026-01-09T19:32:17Z
dc.date.copyright2022-07-01
dc.date.issued2022
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.number1
dc.description.points20
dc.description.versionfinal_published
dc.description.volume12
dc.identifier.doi10.12785/ijcds/1201019
dc.identifier.issn2210-142X
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/6697
dc.identifier.weblinkhttps://journal.uob.edu.bh/items/f995600a-ef2c-4ae3-a66b-95aee6a0b281
dc.languageen
dc.relation.ispartofInternational Journal of Computing and Digital Systems
dc.relation.pages215-223
dc.rightsOther
dc.sciencecloudnosend
dc.share.typeOPEN_JOURNAL
dc.subject.endeep reinforcement learning (DRL)
dc.subject.enmixed-integer nonlinear programming (MINLP)
dc.subject.enadditive white Gaussian noise (AWGN)
dc.subject.enFourier transform (FT)
dc.subject.enfifth generation (5G).
dc.titleAnalysis of the Fifth Generation NOMA System Using LSTMAlgorithm
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue1
oaire.citation.volume12