{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T08:14:51Z","timestamp":1783066491279,"version":"3.54.6"},"reference-count":61,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T00:00:00Z","timestamp":1718841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Joint source\u2013channel coding (JSCC) based on deep learning has shown significant advancements in image transmission tasks. However, previous channel-adaptive JSCC methods often rely on the signal-to-noise ratio (SNR) of the current channel for encoding, which overlooks the neural network\u2019s self-adaptive capability across varying SNRs. This paper investigates the self-adaptive capability of deep learning-based JSCC models to dynamically changing channels and introduces a novel method named Channel-Blind JSCC (CBJSCC). CBJSCC leverages the intrinsic learning capability of neural networks to self-adapt to dynamic channels and diverse SNRs without relying on external SNR information. This approach is advantageous, as it is not affected by channel estimation errors and can be applied to one-to-many wireless communication scenarios. To enhance the performance of JSCC tasks, the CBJSCC model employs a specially designed encoder\u2013decoder. Experimental results show that CBJSCC outperforms existing channel-adaptive JSCC methods that depend on SNR estimation and feedback, both in additive white Gaussian noise environments and under slow Rayleigh fading channel conditions. Through a comprehensive analysis of the model\u2019s performance, we further validate the robustness and adaptability of this strategy across different application scenarios, with the experimental results providing strong evidence to support this claim.<\/jats:p>","DOI":"10.3390\/s24124005","type":"journal-article","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T03:44:49Z","timestamp":1718941489000},"page":"4005","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Channel-Blind Joint Source\u2013Channel Coding for Wireless Image Transmission"],"prefix":"10.3390","volume":"24","author":[{"given":"Hongjie","family":"Yuan","sequence":"first","affiliation":[{"name":"State Key Laboratory of Media Convergence & Communication, Communication University of China, Beijing 100024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weizhang","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Media Convergence & Communication, Communication University of China, Beijing 100024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhuan","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Media Convergence & Communication, Communication University of China, Beijing 100024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingxing","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Media Convergence & Communication, Communication University of China, Beijing 100024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1109\/TIT.2003.810631","article-title":"To code, or not to code: Lossy source-channel communication revisited","volume":"49","author":"Gastpar","year":"2003","journal-title":"IEEE Trans. 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