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The proposed approach in millimeter wave transmission is a fully connected analog phase\u2010shifter\u2010based BF architecture with limited radio frequency chains and imperfect channel state information (CSI). Deep learning (DL) is a powerful method for channel estimation and signal identification in wireless communications. Hence, this research proposes a DL\u2010enabled beamforming neural network (BFNN) which can be programmed to optimize the beamformer to attain better spectral efficiency. Simulation findings reveal that the proposed BFNN achieves significant performance gain and high robustness to imperfect CSI. The proposed BFNN greatly decreases the computational complexity by 0.16 million floating point operations (FLOPs) over 0.26 million FLOPs by conventional BF algorithms.<\/jats:p>","DOI":"10.1002\/dac.5109","type":"journal-article","created":{"date-parts":[[2022,2,3]],"date-time":"2022-02-03T09:20:38Z","timestamp":1643880038000},"update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Beamforming design with fully connected analog beamformer using deep learning"],"prefix":"10.1002","volume":"35","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-2043-790X","authenticated-orcid":false,"given":"Jeyakumar","family":"P.","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering M.Kumarasamy College of Engineering  Karur Tamilnadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tharanitaran 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