Robust Deep Joint Source-Channel Coding Enabled Distributed Image Transmission with Imperfect Channel State Information

[2411.12228] Robust Deep Joint Source-Channel Coding Enabled Distributed Image Transmission with Imperfect Channel State Information Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Signal Processing arXiv:2411.12228 (eess) [Submitted on 19 Nov 2024 ( v1 ), last revised 14 Apr 2026 (this version, v3)] Title: Robust Deep Joint Source-Channel Coding Enabled Distributed Image Transmission with Imperfect Channel State Information Authors: Biao Dong , Bin Cao , Guan Gui , Qinyu Zhang View a PDF of the paper titled Robust Deep Joint Source-Channel Coding Enabled Distributed Image Transmission with Imperfect Channel State Information, by Biao Dong and 3 other authors View PDF HTML (experimental) Abstract: This work is concerned with robust distributed multi-view image transmission over a severe fading channel with imperfect channel state information (CSI), wherein the sources are slightly correlated. Since the signals are further distorted at the decoder, traditional distributed deep joint source-channel coding (DJSCC) suffers considerable performance degradation. To tackle this problem, we leverage the complementarity and consistency characteristics among the distributed, yet correlated sources, and propose an enhanced robust DJSCC, namely RDJSCC. In RDJSCC, we design a novel cross-view information extraction (CVIE) mechanism to capture more nuanced cross-view patterns and dependencies. In addition, a complementarity-consistency fusion (CCF) mechanism is utilized to fuse the complementarity and consistency from multi-view information in a symmetric and compact manner. Theoretical analysis and simulation results show that our proposed RDJSCC can effectively leverage the advantages of correlated sources even under severe fading conditions, leading to an improved reconstruction performance. The open source code of this work is available at: this https URL . Subjects: Signal Processing (eess.SP) Cite as: arXiv:2411.12228 [eess.SP] (or arXiv:2411.12228v3 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2411.12228 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Biao Dong [ view email ] [v1] Tue, 19 Nov 2024 04:48:18 UTC (6,638 KB) [v2] Tue, 24 Jun 2025 05:52:51 UTC (5,235 KB) [v3] Tue, 14 Apr 2026 04:23:57 UTC (12,894 KB) Full-text links: Access Paper: View a PDF of the paper titled Robust Deep Joint Source-Channel Coding Enabled Distributed Image Transmission with Imperfect Channel State Information, by Biao Dong and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2024-11 Change to browse by: eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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