Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO

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dc.authoridElbir, Ahmet M./0000-0003-4060-3781
dc.authoridCöleri, Sinem/0000-0002-7502-3122
dc.authorwosidElbir, Ahmet M./X-3731-2019
dc.authorwosidCöleri, Sinem/O-9829-2014
dc.contributor.authorElbir, Ahmet M.
dc.contributor.authorCöleri, Sinem
dc.date.accessioned2023-07-26T11:58:00Z
dc.date.available2023-07-26T11:58:00Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractMachine learning (ML) has attracted a great research interest for physical layer design problems, such as channel estimation, thanks to its low complexity and robustness. Channel estimation via ML requires model training on a dataset, which usually includes the received pilot signals as input and channel data as output. In previous works, model training is mostly done via centralized learning (CL), where the whole training dataset is collected from the users at the base station (BS). This approach introduces huge communication overhead for data collection. In this paper, to address this challenge, we propose a federated learning (FL) framework for channel estimation. We design a convolutional neural network (CNN) trained on the local datasets of the users without sending them to the BS. We develop FL-based channel estimation schemes for both conventional and RIS (intelligent reflecting surface) assisted massive MIMO (multiple-input multiple-output) systems, where a single CNN is trained for two different datasets for both scenarios. We evaluate the performance for noisy and quantized model transmission and show that the proposed approach provides approximately 16 times lower overhead than CL, while maintaining satisfactory performance close to CL. Furthermore, the proposed architecture exhibits lower estimation error than the state-of-the-art ML-based schemes.en_US
dc.description.sponsorshipEuropean Coordinated Research on Long-term Challenges in Information and Communication Sciences and Technologies (CHIST-ERA) [CHIST-ERA-18-SDCDN-001]; Scientific and Technological Council of Turkey [119E350]en_US
dc.description.sponsorshipThe work of Sinem Coleri was supported in part by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences and Technologies (CHIST-ERA) under Grant CHIST-ERA-18-SDCDN-001 and in part by the Scientific and Technological Council of Turkey under Grant 119E350.en_US
dc.identifier.doi10.1109/TWC.2021.3128392
dc.identifier.endpage4268en_US
dc.identifier.issn1536-1276
dc.identifier.issn1558-2248
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85120546477en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage4255en_US
dc.identifier.urihttps://doi.org/10.1109/TWC.2021.3128392
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13370
dc.identifier.volume21en_US
dc.identifier.wosWOS:000809406400051en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorElbir, Ahmet M.
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions on Wireless Communicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectChannel Estimation; Training; Data Models; Computational Modeling; Radio Frequency; Massive Mimo; Wireless Communication; Channel Estimation; Federated Learning; Machine Learning; Centralized Learning; Massive Mimoen_US
dc.subjectReconfigurable Intelligent Surfaces; Beamforming Design; Antenna Selection; Deep; Systemsen_US
dc.titleFederated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMOen_US
dc.typeArticleen_US

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