Cognitive Learning-Aided Multi-Antenna Communications

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dc.contributor.authorElbir, Ahmet M.
dc.contributor.authorMishra, Kumar Vijay
dc.date.accessioned2023-07-26T11:58:39Z
dc.date.available2023-07-26T11:58:39Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractCognitive communications have emerged as a promising solution to enhance, adapt, and invent new tools and capabilities that transcend conventional wireless networks. Deep learning (DL) is critical in enabling essential features of cognitive systems because of its fast prediction performance, adaptive behavior, and model-free structure. These features are especially significant for multi-antenna wireless communications systems, which generate and handle massive data. Multiple antennas may provide multiplexing, diversity, or antenna gains that improve the capacity, bit error rate, or the signal-to-interference-plus-noise ratio, respectively. In practice, multi-antenna cognitive communications encounter challenges in terms of data complexity and diversity, hardware complexity, and wireless channel dynamics. DL solutions such as federated learning, transfer learning, and online learning tackle these problems at various stages of communications processing, including multi-channel estimation, hybrid beamforming, user localization, and sparse array design. This article provides a synopsis of various DL-based methods to impart cognitive behavior to multi-antenna wireless communications for improved robustness and adaptation to the environmental changes while providing satisfactory spectral efficiency and computation times. We discuss DL design challenges from the perspective of data, learning and transceiver architectures. In particular, we suggest quantized learning models, data/model parallelization, and distributed learning methods to address the aforementioned challenges.en_US
dc.identifier.doi10.1109/MWC.008.2100416
dc.identifier.endpage143en_US
dc.identifier.issn1536-1284
dc.identifier.issn1558-0687
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85132510949en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage136en_US
dc.identifier.urihttps://doi.org/10.1109/MWC.008.2100416
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13538
dc.identifier.volume29en_US
dc.identifier.wosWOS:000917339700019en_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 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; Wireless Communication; Data Models; Symbols; Array Signal Processing; Antennas; Trainingen_US
dc.titleCognitive Learning-Aided Multi-Antenna Communicationsen_US
dc.typeArticleen_US

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