Channels with Markov Synchronization Errors: Information Stability and Capacity Bounds

[2401.16063] Channels with Markov Synchronization Errors: Information Stability and Capacity Bounds Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Information Theory arXiv:2401.16063 (cs) [Submitted on 29 Jan 2024 ( v1 ), last revised 11 Apr 2026 (this version, v6)] Title: Channels with Markov Synchronization Errors: Information Stability and Capacity Bounds Authors: Ruslan Morozov , Tolga M. Duman View a PDF of the paper titled Channels with Markov Synchronization Errors: Information Stability and Capacity Bounds, by Ruslan Morozov and Tolga M. Duman View PDF HTML (experimental) Abstract: Particularly motivated by DNA storage channels, we consider channels with synchronization errors modeled as insertions and deletions, along with substitutions. We focus on the case where the synchronization error process has memory and investigate the information stability of these channels, hence the existence of their Shannon capacity. We assume that the synchronization errors are governed by a stationary and ergodic finite state Markov chain and prove that such a channel is information-stable, which implies the existence of a coding scheme that achieves the limit of mutual information. This result implies the existence of the Shannon capacity for a wide range of channels with synchronization errors, with different applications, including DNA storage. We also provide specific examples of deletion channels with Markov memory and numerically evaluate their capacity bounds, thereby allowing us to quantify the capacity difference between memoryless deletion channels and those with memory with the same deletion probability and reveal that having memory increases the channel capacity. Comments: 13 pages, 3 figures Subjects: Information Theory (cs.IT) MSC classes: 94A24 Cite as: arXiv:2401.16063 [cs.IT] (or arXiv:2401.16063v6 [cs.IT] for this version) https://doi.org/10.48550/arXiv.2401.16063 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ruslan Morozov [ view email ] [v1] Mon, 29 Jan 2024 11:18:29 UTC (27 KB) [v2] Tue, 30 Jan 2024 10:44:21 UTC (27 KB) [v3] Tue, 26 Mar 2024 18:57:17 UTC (26 KB) [v4] Tue, 7 Jan 2025 07:48:04 UTC (42 KB) [v5] Mon, 12 May 2025 10:58:47 UTC (57 KB) [v6] Sat, 11 Apr 2026 10:27:22 UTC (45 KB) Full-text links: Access Paper: View a PDF of the paper titled Channels with Markov Synchronization Errors: Information Stability and Capacity Bounds, by Ruslan Morozov and Tolga M. Duman View PDF HTML (experimental) TeX Source view license Current browse context: cs.IT < prev | next > new | recent | 2024-01 Change to browse by: cs math math.IT 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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