Selective Interference Suppression of Siamese-Net in Heterogeneous Interference Channels

[2608.27635] Selective Interference Suppression of Siamese-Net in Heterogeneous Interference Channels Skip to main content Search Submit Donate Log in Computer Science > Information Theory arXiv:2608.27635 (cs) This paper has been withdrawn by Arkadeep Sinha [Submitted on 27 Aug 2026 ( v1 ), last revised 18 Sep 2026 (this version, v2)] Title: Selective Interference Suppression of Siamese-Net in Heterogeneous Interference Channels Authors: Arkadeep Sinha , Shubham Paul , R. Manivasakan , Nambi Seshadri , R. David Koilpillai No PDF available, click to view other formats Abstract: We study an end-to-end learnt short-block codes for a $N$-user real Gaussian interference channel with heterogeneous pairwise interference strengths, while keeping single-user decoding at every receiver. In this paper, we study the case wherein only a few dominant interferers exist and investigate whether Siamese-style coupled training can adapt selectively to encode (\& decode) to ensure optimal performance corresponding to best tradeoff between orthogonality and coding gain or it enforces unnecessary global orthogonality oblivious of the reality. Our work focuses on a 4-user unequal-interference configuration with one dominant pair $(1,2)$ (of strength $\Lambda$) and a weak pair (with weak background coupling ($\lambda$)), through which we demonstrate a selective interference suppression phenomenon where the learned codebooks become near-orthogonal primarily for the dominant pair, while weakly coupled pairs retain alignment needed for coding gain. We quantify this behaviour using latent-space cross-user similarity statistics (worst-case coherence measure, average similarity measure, etc) and connect these geometric signatures to the observed BLER robustness under unequal interference. It seems that the SiameseNet selectively suppresses the interferences from various interferring user pairs to yield optimal tradeoff between coding gain and BLER dictated by orthogonality. Comments: Results in paper maybe erroneous. Needs further validation Subjects: Information Theory (cs.IT) Cite as: arXiv:2608.27635 [cs.IT] (or arXiv:2608.27635v2 [cs.IT] for this version) https://doi.org/10.48550/arXiv.2608.27635 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Arkadeep Sinha [ view email ] [v1] Thu, 27 Aug 2026 19:19:19 UTC (2,243 KB) [v2] Fri, 18 Sep 2026 04:24:01 UTC (1 KB) (withdrawn) Full-text links: Access Paper: Withdrawn Additional Features Audio Summary Current browse context: cs.IT < prev | next > new | recent | 2026-08 Change to browse by: cs 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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