{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T01:00:35Z","timestamp":1783645235658,"version":"3.55.0"},"reference-count":36,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:00:00Z","timestamp":1740096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Laboratory of Advanced Communication Networks","award":["SCX23641X011"],"award-info":[{"award-number":["SCX23641X011"]}]},{"name":"National Key Laboratory of Advanced Communication Networks","award":["F2024523005"],"award-info":[{"award-number":["F2024523005"]}]},{"name":"National Key Laboratory of Advanced Communication Networks","award":["IFN202404"],"award-info":[{"award-number":["IFN202404"]}]},{"name":"Natural Science Foundation of Hebei Province","award":["SCX23641X011"],"award-info":[{"award-number":["SCX23641X011"]}]},{"name":"Natural Science Foundation of Hebei Province","award":["F2024523005"],"award-info":[{"award-number":["F2024523005"]}]},{"name":"Natural Science Foundation of Hebei Province","award":["IFN202404"],"award-info":[{"award-number":["IFN202404"]}]},{"name":"National Key Laboratory of Wireless Communications Foundation","award":["SCX23641X011"],"award-info":[{"award-number":["SCX23641X011"]}]},{"name":"National Key Laboratory of Wireless Communications Foundation","award":["F2024523005"],"award-info":[{"award-number":["F2024523005"]}]},{"name":"National Key Laboratory of Wireless Communications Foundation","award":["IFN202404"],"award-info":[{"award-number":["IFN202404"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Research on deep learning (DL)-based channel estimation for massive multiple-input multiple-output (MIMO) communication systems has attracted considerable interest in recent years. In this paper, we propose a DL-assisted channel estimation algorithm that transforms the original channel estimation problem into an image denoising problem, contrasting it with traditional experience-based channel estimation methods. We establish a new polarized self-attention-aided channel estimation neural network (PACE-Net) to achieve efficient channel estimation. This approach addresses the limitations of the conventional methods, particularly their low accuracy and high computational complexity. In addition, we construct a channel dataset to facilitate the training and testing of PACE-Net. The simulation results show that the proposed DL-assisted channel estimation algorithm has better normalization mean square error (NMSE) performance compared with the traditional algorithms and other DL-assisted algorithms. Furthermore, the computational complexity of the proposed DL-assisted algorithm is significantly lower than that of the traditional minimum mean square error (MMSE) channel estimation algorithm.<\/jats:p>","DOI":"10.3390\/e27030220","type":"journal-article","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:47:46Z","timestamp":1740109666000},"page":"220","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Channel Estimation for Massive MIMO Systems via Polarized Self-Attention-Aided Channel Estimation Neural Network"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1572-2684","authenticated-orcid":false,"given":"Shuo","family":"Yang","sequence":"first","affiliation":[{"name":"54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China"},{"name":"National Key Laboratory of Advanced Communication Networks, Shijiazhuang 050081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China"},{"name":"National Key Laboratory of Advanced Communication Networks, Shijiazhuang 050081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lizhe","family":"Liu","sequence":"additional","affiliation":[{"name":"54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China"},{"name":"National Key Laboratory of Advanced Communication Networks, Shijiazhuang 050081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Xia","sequence":"additional","affiliation":[{"name":"54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China"},{"name":"National Key Laboratory of Advanced Communication Networks, Shijiazhuang 050081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0668-7080","authenticated-orcid":false,"given":"Xingjian","family":"Li","sequence":"additional","affiliation":[{"name":"54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China"},{"name":"National Key Laboratory of Advanced Communication Networks, Shijiazhuang 050081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1109\/JSTSP.2014.2317671","article-title":"An overview of massive MIMO: Benefits and challenges","volume":"8","author":"Lu","year":"2014","journal-title":"IEEE J. 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