Hybrid Precoding for Multiuser Millimeter Wave Massive MIMO Systems: A Deep Learning Approach

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dc.authoridElbir, Ahmet M./0000-0003-4060-3781
dc.authoridPapazafeiropoulos, Anastasios/0000-0003-1841-6461
dc.authorwosidElbir, Ahmet M./X-3731-2019
dc.contributor.authorElbir, Ahmet M.
dc.contributor.authorPapazafeiropoulos, Anastasios K.
dc.date.accessioned2021-12-01T18:49:16Z
dc.date.available2021-12-01T18:49:16Z
dc.date.issued2020
dc.department[Belirlenecek]en_US
dc.description.abstractIn multi-user millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems, hybrid precoding is a crucial task to lower the complexity and cost while achieving a sufficient sum-rate. Previous works on hybrid precoding were usually based on optimization or greedy approaches. These methods either provide higher complexity or have sub-optimum performance. Moreover, the performance of these methods mostly relies on the quality of the channel data. In this work, we propose a deep learning (DL) framework to improve the performance and provide less computation time as compared to conventional techniques. In fact, we design a convolutional neural network for MIMO (CNN-MIMO) that accepts as input an imperfect channel matrix and gives the analog precoder and combiners at the output. The procedure includes two main stages. First, we develop an exhaustive search algorithm to select the analog precoder and combiners from a predefined codebook maximizing the achievable sum-rate. Then, the selected precoder and combiners are used as output labels in the training stage of CNN-MIMO where the input-output pairs are obtained. We evaluate the performance of the proposed method through numerous and extensive simulations and show that the proposed DL framework outperforms conventional techniques. Overall, CNN-MIMO provides a robust hybrid precoding scheme in the presence of imperfections regarding the channel matrix. On top of this, the proposed approach exhibits less computation time with comparison to the optimization and codebook based approaches.en_US
dc.identifier.doi10.1109/TVT.2019.2951501
dc.identifier.endpage563en_US
dc.identifier.issn0018-9545
dc.identifier.issn1939-9359
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85078457888en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage552en_US
dc.identifier.urihttps://doi.org/10.1109/TVT.2019.2951501
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10693
dc.identifier.volume69en_US
dc.identifier.wosWOS:000512550600045en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions On Vehicular Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHybrid precodingen_US
dc.subjectmmWave systemsen_US
dc.subjectmulti-user MIMO transmissionen_US
dc.subjectdeep learningen_US
dc.subjectconvolutional neural networksen_US
dc.subjectChannel Estimationen_US
dc.subjectAntenna Selectionen_US
dc.subjectCombiner Designen_US
dc.subjectCapacityen_US
dc.subjectModelsen_US
dc.subjectSignalen_US
dc.titleHybrid Precoding for Multiuser Millimeter Wave Massive MIMO Systems: A Deep Learning Approachen_US
dc.typeArticleen_US

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