Low-Complexity Limited-Feedback Deep Hybrid Beamforming for Broadband Massive MIMO

Basit Öğe Kaydı
dc.contributor.authorElbir, Ahmet M.
dc.contributor.authorMishra, Kumar Vijay
dc.date.accessioned2021-12-01T18:48:50Z
dc.date.available2021-12-01T18:48:50Z
dc.date.issued2020
dc.department[Belirlenecek]en_US
dc.description21st IEEE International Workshop on Signal Processing Advances in Wireless Communications (IEEE SPAWC) -- MAY 26-29, 2020 -- ELECTR NETWORKen_US
dc.description.abstractThe broadband millimeter-wave (mm-Wave) systems use hybrid beamformers with common analog beamformer for the entire band while employing different baseband beamformers in different frequency sub-bands. Furthermore, the performance mostly relies on the perfectness of the channel information. In this paper, we propose a deep learning (DL) framework for hybrid beamformer design in broadband mmWave massive MIMO systems. We design a convolutional neural network (CNN) that accepts the channel matrix of all subcarriers as input and the output of CNN is the hybrid beamformers. The proposed CNN architecture is trained with imperfect channel matrices in order to provide robust performance against the deviations in the channel data. Hence, the proposed precoding scheme can handle the imperfect or limited feedback scenario where the full and exact knowledge of the channel is not available. We show that the proposed DL framework is more robust and computationally less complex than the conventional optimization and phase-extraction-based approaches.en_US
dc.description.sponsorshipIEEEen_US
dc.identifier.isbn978-1-7281-5478-7
dc.identifier.issn2325-3789
dc.identifier.scopus2-s2.0-85090395670en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10614
dc.identifier.wosWOS:000620337500116en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartofProceedings Of The 21St Ieee International Workshop On Signal Processing Advances In Wireless Communications (Ieee Spawc2020)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep learningen_US
dc.subjecthybrid beamformingen_US
dc.subjectmassive MIMOen_US
dc.subjectmillimeter-wave communicationsen_US
dc.subjectwidebanden_US
dc.subjectChannel Estimationen_US
dc.subjectAntenna Selectionen_US
dc.subjectWaveen_US
dc.subjectDesignen_US
dc.titleLow-Complexity Limited-Feedback Deep Hybrid Beamforming for Broadband Massive MIMOen_US
dc.typeConference Objecten_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
10614.pdf
Boyut:
414.04 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu