{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T07:23:20Z","timestamp":1780730600877,"version":"3.54.1"},"reference-count":34,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T00:00:00Z","timestamp":1645142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guizhou Science and Technology Foundation","award":["No. [2016] 1054"],"award-info":[{"award-number":["No. [2016] 1054"]}]},{"name":"Guizhou Province Joint funding Project","award":["LH [2017] 7226"],"award-info":[{"award-number":["LH [2017] 7226"]}]},{"name":"Guizhou University Academic New Seeding training and Innovation and Exploration Project","award":["No.[2017] 5788"],"award-info":[{"award-number":["No.[2017] 5788"]}]},{"name":"Social Development Project of Guizhou Province","award":["No. [2011]3111"],"award-info":[{"award-number":["No. [2011]3111"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Channel estimation is a challenging task in a millimeter-wave (mm Wave) massive multiple-input multiple-output (MIMO) system. The existing deep learning scheme, which learns the mapping from the input to the target channel, has great difficulty in estimating the exact channel state information (CSI). In this paper, we consider the quantized received measurements as a low-resolution image, and we adopt the deep learning-based image super-resolution technique to reconstruct the mm Wave channel. Specifically, we exploit a state-of-the-art channel estimation framework based on residual learning and multi-path feature fusion (RL-MFF-Net). Firstly, residual learning makes the channel estimator focus on learning high-frequency residual information between the quantized received measurements and the mm Wave channel, while abundant low-frequency information is bypassed through skip connections. Moreover, to address the estimator\u2019s gradient dispersion problem, a dense connection is added to the residual blocks to ensure the maximum information flow between the layers. Furthermore, the underlying mm Wave channel local features extracted from different residual blocks are preserved by multi-path feature fusion. The simulation results demonstrate that the proposed scheme outperforms traditional methods as well as existing deep learning methods, especially in the low signal-to-noise-ration (SNR) region.<\/jats:p>","DOI":"10.3390\/e24020292","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T08:18:05Z","timestamp":1645431485000},"page":"292","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Residual Learning and Multi-Path Feature Fusion-Based Channel Estimation for Millimeter-Wave Massive MIMO System"],"prefix":"10.3390","volume":"24","author":[{"given":"Xuhui","family":"Zheng","sequence":"first","affiliation":[{"name":"College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9191-2790","authenticated-orcid":false,"given":"Ziyan","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China"},{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingyu","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunlei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/MCOM.2011.5783993","article-title":"An introduction to millimeter-wave mobile broadband systems","volume":"49","author":"Pi","year":"2011","journal-title":"IEEE Commun. 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