Flexible Deep Joint Source-Channel Coding: A Vibrotactile Example

[2608.15070] Flexible Deep Joint Source-Channel Coding: A Vibrotactile Example 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:2608.15070 (eess) [Submitted on 15 Aug 2026] Title: Flexible Deep Joint Source-Channel Coding: A Vibrotactile Example Authors: Shuijie Li , Kemi Chen , Runjie Wang , Tiesong Zhao , Xiaoming Tao View a PDF of the paper titled Flexible Deep Joint Source-Channel Coding: A Vibrotactile Example, by Shuijie Li and 4 other authors View PDF HTML (experimental) Abstract: The increasing demand for real-time tactile communication in multimedia systems has exposed the limitations of existing Joint Source-Channel Coding (JSCC) techniques. While current JSCC models facilitate end-to-end optimization, they typically operate at fixed coding rates and require separate model instances for different rate settings. This results in significant storage overhead and limited adaptability to dynamic bandwidth conditions. To address these challenges, we propose the Flexible Deep Joint Source-Channel Coding (FD-JSCC) framework for vibrotactile signals, which supports flexible-rate transmission without the need for model switching. The FD-JSCC integrates a flexible-rate encoder-decoder enhanced with Hierarchical Gain Adaptation Module (HGAM) and Rate-Switchable Residual Module (RSRM), enabling bitrate-aware compression by selectively preserving salient vibrotactile features. Additionally, we introduce a Channel Feature Processing Module (CFPM), which leverages real-time SNR information to enhance robustness against channel noise and signal degradation. Trained on the IEEE 1918.1.1 vibrotactile dataset, FD-JSCC achieves reconstruction performance comparable to fixed-rate baselines (e.g., DeepSC-S), while reducing storage requirements by 61.1\% when supporting four rates. These results underscore its potential for scalable, low-latency tactile communication in next-generation networks. Subjects: Signal Processing (eess.SP) ; Multimedia (cs.MM); Image and Video Processing (eess.IV) Cite as: arXiv:2608.15070 [eess.SP] (or arXiv:2608.15070v1 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2608.15070 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Kemi Chen [ view email ] [v1] Sat, 15 Aug 2026 06:41:11 UTC (5,433 KB) Full-text links: Access Paper: View a PDF of the paper titled Flexible Deep Joint Source-Channel Coding: A Vibrotactile Example, by Shuijie Li and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2026-08 Change to browse by: cs cs.MM eess eess.IV 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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