{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:54:19Z","timestamp":1783184059929,"version":"3.54.6"},"reference-count":33,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T00:00:00Z","timestamp":1647993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In this paper, we propose an end-to-end deep learning approach to realize channel state information (CSI) feedback and hybrid precoding for millimeter wave massive multiple-input multiple-output systems in the frequency division duplexing mode. Different from conventional approaches that treat the CSI reconstruction and hybrid precoding as separate components, we propose a new end-to-end learning method bypassing the channel reconstruction phase, and design the hybrid precoders and combiners directly from the feedback codewords (a compressed version of the CSI). More specifically, we design a neural network composed of the CSI feedback and hybrid precoding. Experiment results show that our proposed network can achieve better performance than conventional hybrid precoding schemes that reserve channel reconstruction, especially when the feedback resources are limited.<\/jats:p>","DOI":"10.3390\/e24040441","type":"journal-article","created":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T12:20:25Z","timestamp":1648038025000},"page":"441","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Deep Learning-Based Joint CSI Feedback and Hybrid Precoding in FDD mmWave Massive MIMO Systems"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6484-4531","authenticated-orcid":false,"given":"Qiang","family":"Sun","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Nantong University, Nantong 226019, China"},{"name":"Nantong Research Institute for Advanced Communication Technologies (NRIACT), Nantong 226019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Nantong University, Nantong 226019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jue","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Nantong University, Nantong 226019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3316-0417","authenticated-orcid":false,"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Nantong University, Nantong 226019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1109\/JSTSP.2016.2520912","article-title":"Hybrid Digital and Analog Beamforming Design for Large-Scale Antenna Arrays","volume":"10","author":"Sohrabi","year":"2016","journal-title":"IEEE J. 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