Deep Learning Aided Channel Estimation Approach for 5G Communication Systems

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dc.authoridKabalci, Yasin/0000-0003-1240-817X
dc.authoridMUTLU, URAL/0000-0003-2595-0531
dc.contributor.authorMutlu, Ural
dc.contributor.authorKabalci, Yasin
dc.date.accessioned2024-11-07T13:23:57Z
dc.date.available2024-11-07T13:23:57Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description4th IEEE Global Power, Energy and Communication Conference (IEEE GPECOM) -- JUN 14-17, 2022 -- Cappadocia, TURKEY
dc.description.abstractThe defining feature of the Fifth Generation (5G) mobile communication systems is going to be Multiple Input Multiple Output (MIMO) transmission scheme, which utilizes the multipath diversities to achieve beamforming and increase spectral efficiency. However, these MIMO algorithms rely on accurate channel parameters. To improve the accuracy of the channel coefficients, the study presents a Deep Learning (DL) based approach that uses the 5G Demodulation Reference Signals (DMRS) as training sequence and Deep Neural Networks (DNN) as training and prediction network in a MIMO scenario. The DNN is trained with training data obtained by applying Least Squares (LS) method to the received pilot signals and by comparing it to Clustered Delay Line (CDL) channel model. The DNN is then used to predict real-time channel coefficients. The results show that the model improves channel estimation performance by reducing the effects of noise, thus improving the Normalized Mean Square Error (NMSE) versus Signal-to-Noise Ratio (SNR) metric of the MIMO system.
dc.description.sponsorshipIEEE,IEEE Ind Applicat Soc,IEEE Ind Elect Soc,IEEE Power & Energy Soc,IEEE Power Elect Soc,Nevsehir Haci Bektas Veli Univ,Univ Nova Lisboa,Univ Palermo
dc.identifier.doi10.1109/GPECOM55404.2022.9815811
dc.identifier.endpage660
dc.identifier.isbn978-1-6654-6925-8
dc.identifier.scopus2-s2.0-85134880032
dc.identifier.scopusqualityN/A
dc.identifier.startpage655
dc.identifier.urihttps://doi.org/10.1109/GPECOM55404.2022.9815811
dc.identifier.urihttps://hdl.handle.net/11480/13826
dc.identifier.wosWOS:000854056400114
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2022 Ieee 4th Global Power, Energy and Communication Conference (Ieee Gpecom2022)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subject5G
dc.subjectChannel Estimation
dc.subjectDMRS
dc.subjectDeep Learning
dc.subjectConvolutional Neural Network
dc.titleDeep Learning Aided Channel Estimation Approach for 5G Communication Systems
dc.typeConference Object

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