M-QAM demodulation based on machine learning

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dc.contributor.authorToledo R.N.
dc.contributor.authorAkamine C.
dc.contributor.authorJerji F.
dc.contributor.authorSilva L.A.
dc.date.accessioned2024-03-12T23:46:19Z
dc.date.available2024-03-12T23:46:19Z
dc.date.issued2020
dc.description.abstract© 2020 IEEE.This paper presents a new Quadrature Amplitude Modulation (M-QAM) demodulation method using Machine Learning techniques. The new method significantly reduces the demodulation complexity for high-order constellations while maintains the demodulation accuracy. The experimental results demonstrate a performance gain of up to 1485% for 4096-QAM in comparison with the classical Log-Likelihood Ratio demodulator.
dc.description.volume2020-October
dc.identifier.doi10.1109/BMSB49480.2020.9379442
dc.identifier.issn2155-5052
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/34887
dc.relation.ispartofIEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB
dc.rightsAcesso Restrito
dc.subject.otherlanguageDemodulation
dc.subject.otherlanguageLLR
dc.subject.otherlanguageM-QAM
dc.subject.otherlanguageMachine Learning
dc.titleM-QAM demodulation based on machine learning
dc.typeArtigo de evento
local.scopus.citations4
local.scopus.eid2-s2.0-85103469923
local.scopus.subjectDemodulation complexity
local.scopus.subjectDemodulation method
local.scopus.subjectHigh-order
local.scopus.subjectLog likelihood ratio
local.scopus.subjectMachine learning techniques
local.scopus.subjectOn-machines
local.scopus.subjectPerformance Gain
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85103469923&origin=inward
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