FEDERATED CHANNEL LEARNING FOR INTELLIGENT REFLECTING SURFACES WITH FEWER PILOT SIGNALS

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dc.contributor.authorElbir, Ahmet M.
dc.contributor.authorCöleri, Sinem
dc.contributor.authorMishra, Kumar Vijay
dc.date.accessioned2023-07-26T11:57:23Z
dc.date.available2023-07-26T11:57:23Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.descriptionIEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM) -- JUN 20-23, 2022 -- Trondheim, NORWAYen_US
dc.description.abstractChannel estimation is a critical task in intelligent reflecting surface (IRS)-assisted wireless systems due to the uncertainties imposed by environment dynamics and rapid changes in the IRS configuration. To deal with these uncertainties, deep learning (DL) approaches have been proposed. Previous works consider centralized learning (CL) approach for model training, which entails the collection of the whole training dataset from the users at the base station (BS), hence introducing huge transmission overhead for data collection. To address this challenge, this paper proposes a federated learning (FL) framework to jointly estimate both direct and cascaded channels in IRSassisted wireless systems. We design a single convolutional neural network trained on the local datasets of the users without sending them to the BS. We show that the proposed FL-based channel estimation approach requires approximately 60% fewer pilot signals and it exhibits 12 times lower transmission overhead than CL, while maintaining satisfactory performance close to CL. In addition, it provides lower estimation error than the state-of-the-art DL-based schemes.en_US
dc.description.sponsorshipFord Otosan; Scientific and Technological Research Council of Turkey EU CHIST-ERA [119E350]en_US
dc.description.sponsorshipS. C. acknowledges the support of Ford Otosan and the Scientific and Technological Research Council of Turkey EU CHIST-ERA grant 119E350.en_US
dc.identifier.doi10.1109/SAM53842.2022.9827849
dc.identifier.endpage230en_US
dc.identifier.isbn978-1-6654-0633-8
dc.identifier.scopus2-s2.0-85135373114en_US
dc.identifier.startpage226en_US
dc.identifier.urihttps://doi.org/10.1109/SAM53842.2022.9827849
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13159
dc.identifier.wosWOS:000922095000046en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorElbir, Ahmet M.
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2022 Ieee 12th Sensor Array and Multichannel Signal Processing Workshop (Sam)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectChannel Estimation; Federated Learning; Intelligent Reflecting Surfaces; Machine Learning; Massive Mimoen_US
dc.subjectWave Massive Mimoen_US
dc.titleFEDERATED CHANNEL LEARNING FOR INTELLIGENT REFLECTING SURFACES WITH FEWER PILOT SIGNALSen_US
dc.typeConference Objecten_US

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