Contextual Memory-Enhanced Source Coding for Low-SNR Communications

[2605.04400] Contextual Memory-Enhanced Source Coding for Low-SNR Communications Skip to main content Search Submit Donate Log in Computer Science > Information Theory arXiv:2605.04400 (cs) [Submitted on 6 May 2026 ( v1 ), last revised 25 Aug 2026 (this version, v3)] Title: Contextual Memory-Enhanced Source Coding for Low-SNR Communications Authors: Ziqiong Wang , Rongpeng Li , Zhifeng Zhao , Honggang Zhang View PDF HTML (experimental) Abstract: Separate Source-Channel Coding (SSCC) remains vulnerable in noisy text transmission due to the fragility of autoregressive source decoding, especially when Arithmetic Coding (AC) relies on Large Language Model (LLM)-based probability estimation. This letter proposes a Memory-Augmented Source Coding (MASC) scheme that internalizes contextual patterns into a source model. Specifically, MASC employs a shared Parameterized Contextual Memory (PCM) for multi-order $n$-gram patterns, and a Mixture-of-Memory-Experts Router (MMER) for sparse, hidden-state-dependent routing over memory experts. This adaptive activation refines source probability estimation, shortens codelength, and mitigates decoding sensitivity to residual channel errors. Experiments over Rayleigh fading and AWGN channels demonstrate its effectiveness. Subjects: Information Theory (cs.IT) ; Machine Learning (cs.LG) Cite as: arXiv:2605.04400 [cs.IT] (or arXiv:2605.04400v3 [cs.IT] for this version) https://doi.org/10.48550/arXiv.2605.04400 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ziqiong Wang [ view email ] [v1] Wed, 6 May 2026 01:44:24 UTC (537 KB) [v2] Thu, 7 May 2026 01:29:17 UTC (537 KB) [v3] Tue, 25 Aug 2026 07:27:03 UTC (538 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.IT < prev | next > new | recent | 2026-05 Change to browse by: cs cs.LG math math.IT References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation 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)

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