{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T17:32:42Z","timestamp":1784136762513,"version":"3.55.0"},"reference-count":29,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002855","name":"Ministry of Science and Technology of China, National Key Research and Development Plan","doi-asserted-by":"publisher","award":["2022YFD1901400"],"award-info":[{"award-number":["2022YFD1901400"]}],"id":[{"id":"10.13039\/501100002855","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In wireless communication, to fully utilize the spectrum and energy efficiency of the system, it is necessary to obtain the channel state information (CSI) of the link. However, in Frequency Division Duplexing (FDD) systems, CSI feedback wastes part of the spectrum resources. In order to save spectrum resources, the CSI needs to be compressed. However, many current deep-learning algorithms have complex structures and a large number of model parameters. When the computational and storage resources are limited, the large number of model parameters will decrease the accuracy of CSI feedback, which cannot meet the application requirements. In this paper, we propose a neural network-based CSI feedback model, Mix_Multi_TransNet, which considers both the spatial characteristics and temporal sequence of the channel, aiming to provide higher feedback accuracy while reducing the number of model parameters. Through experiments, it is found that Mix_Multi_TransNet achieves higher accuracy than the traditional CSI feedback network in both indoor and outdoor scenes. In the indoor scene, the NMSE gains of Mix_Multi_TransNet are 4.06 dB, 4.92 dB, 4.82 dB, and 6.47 dB for compression ratio \u03b7 = 1\/8, 1\/16, 1\/32, 1\/64, respectively. In the outdoor scene, the NMSE gains of Mix_Multi_TransNet are 3.63 dB, 6.24 dB, 4.71 dB, 4.60 dB, and 2.93 dB for compression ratio \u03b7 = 1\/4, 1\/8, 1\/16, 1\/32, 1\/64, respectively.<\/jats:p>","DOI":"10.3390\/s23198139","type":"journal-article","created":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T07:50:26Z","timestamp":1695887426000},"page":"8139","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["CSI Feedback Model Based on Multi-Source Characterization in FDD Systems"],"prefix":"10.3390","volume":"23","author":[{"given":"Fei","family":"Pan","sequence":"first","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625014, China"},{"name":"Ya\u2019an Digital Agricultural Engineering Technology Research Center, Ya\u2019an 625014, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0068-9922","authenticated-orcid":false,"given":"Xiaoyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625014, China"},{"name":"Ya\u2019an Digital Agricultural Engineering Technology Research Center, Ya\u2019an 625014, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boda","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625014, China"},{"name":"Ya\u2019an Digital Agricultural Engineering Technology Research Center, Ya\u2019an 625014, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengjun","family":"Xiang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625014, China"},{"name":"Ya\u2019an Digital Agricultural Engineering Technology Research Center, Ya\u2019an 625014, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengdie","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625014, China"},{"name":"Ya\u2019an Digital Agricultural Engineering Technology Research Center, Ya\u2019an 625014, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuesong","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Resources, Sichuan Agricultural University, Chengdu 625099, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Grachev, M., Parshin, A., and Parshin, Y. 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