Efficient Algorithm Design of Dynamic Spectrum Access by Whittle Index

[2501.00236] Efficient Algorithm Design of Dynamic Spectrum Access by Whittle Index Skip to main content Search Submit Donate Log in Mathematics > Optimization and Control arXiv:2501.00236 (math) [Submitted on 31 Dec 2024 ( v1 ), last revised 25 Aug 2026 (this version, v3)] Title: Efficient Algorithm Design of Dynamic Spectrum Access by Whittle Index Authors: Keqin Liu , Qizhen Jia , Yiying Zhang , Zhi Ding View PDF HTML (experimental) Abstract: Dynamic spectrum access problem is an important problem that allows a wireless sub-network to use channels temporarily unoccupied by the parent network for minimizing the spectrum waste. Previous work has shown that the sequential channel allocation problem for the sub-network can be formulated within the restless multi-armed bandits (RMAB) framework. The objective is to maximize the expected long-term return over an infinite horizon while minimizing interference to the parent network. Different from the previous work that exploits a binary feedback (e.g., ACK/NAK) to compensate for sensing errors, we leverage the finer and more robust channel quality indicator (CQI) feedback to update the information state (belief vector) of the sub-network. However, the implementation of CQI-based observation model yields significantly more complex belief transition behaviors in an infinite state space and worsens the curse of dimensionality of dynamic programming. To overcome this challenge, we dive into the rich structures of the value functions and obtain tight bounds on their derivatives. These results lead to the proof of optimality of threshold policies on a single-channel problem with subsidy and subsequently a closed-form channel index function using an iterative method to approximate the well-known Whittle index policy, which offers a low-complexity solution for ranking the currently available channels whose states are never directly observable. Through extensive numerical studies, we demonstrate the superior performance and robustness of our proposed algorithm. Subjects: Optimization and Control (math.OC) Cite as: arXiv:2501.00236 [math.OC] (or arXiv:2501.00236v3 [math.OC] for this version) https://doi.org/10.48550/arXiv.2501.00236 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Keqin Liu Prof. [ view email ] [v1] Tue, 31 Dec 2024 03:00:47 UTC (643 KB) [v2] Thu, 14 May 2026 13:37:28 UTC (2,881 KB) [v3] Tue, 25 Aug 2026 10:03:52 UTC (2,868 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: math.OC < prev | next > new | recent | 2025-01 Change to browse by: math 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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