A Family of Deep Learning Architectures for Channel Estimation and Hybrid Beamforming in Multi-Carrier mm-Wave Massive MIMO

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dc.authoridElbir, Ahmet M./0000-0003-4060-3781
dc.authoridOttersten, Bjorn/0000-0003-2298-6774
dc.authorwosidElbir, Ahmet M./X-3731-2019
dc.authorwosidOttersten, Bjorn/G-1005-2011
dc.contributor.authorElbir, Ahmet M.
dc.contributor.authorMishra, Kumar Vijay
dc.contributor.authorShankar, M. R. Bhavani
dc.contributor.authorOttersten, Bjorn
dc.date.accessioned2023-07-26T11:54:34Z
dc.date.available2023-07-26T11:54:34Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractHybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. However, lack of fully digital beamforming in hybrid architectures and short coherence times at mm-Wave impose additional constraints on the channel estimation. Prior works on addressing these challenges have focused largely on narrowband channels wherein optimization-based or greedy algorithms were employed to derive hybrid beamformers. In this paper, we introduce a deep learning (DL) approach for channel estimation and hybrid beamforming for frequency-selective, wideband mm-Wave systems. In particular, we consider a massive MIMO Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system and propose three different DL frameworks comprising convolutional neural networks (CNNs), which accept the raw data of received signal as input and yield channel estimates and the hybrid beamformers at the output. We also introduce both offline and online prediction schemes. Numerical experiments demonstrate that, compared to the current state-of-the-art optimization and DL methods, our approach provides higher spectral efficiency, lesser computational cost and fewer number of pilot signals, and higher tolerance against the deviations in the received pilot data, corrupted channel matrix, and propagation environment.en_US
dc.description.sponsorshipERC Advanced Grant AGNOSTIC [EC/H2020/ERC2016ADG/742648/AGNOSTIC]en_US
dc.description.sponsorshipThis work was partially funded by the ERC Advanced Grant AGNOSTIC, EC/H2020/ERC2016ADG/742648/AGNOSTIC.en_US
dc.identifier.doi10.1109/TCCN.2021.3132609
dc.identifier.endpage656en_US
dc.identifier.issn2332-7731
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85121357992en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage642en_US
dc.identifier.urihttps://doi.org/10.1109/TCCN.2021.3132609
dc.identifier.urihttps://hdl.handle.net/20.500.12684/12867
dc.identifier.volume8en_US
dc.identifier.wosWOS:000808086800018en_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 Cognitive Communications and Networkingen_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; Deep Learning; Online Learning; Hybrid Beamforming; Mm-Waveen_US
dc.subjectAntenna Selection; Beam Selection; Phase Shifters; Design; Tracking; Precoder; Ofdmen_US
dc.titleA Family of Deep Learning Architectures for Channel Estimation and Hybrid Beamforming in Multi-Carrier mm-Wave Massive MIMOen_US
dc.typeArticleen_US

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