Cited By
View all- Hanawal MDarak S(2022)Multiplayer Bandits: A Trekking ApproachIEEE Transactions on Automatic Control10.1109/TAC.2021.307745467:5(2237-2252)Online publication date: May-2022
Distributed Learning in Ad-Hoc Networks with Unknown Number of Players
Pages 171 - 174
Abstract
We study algorithms for distributed learning in ad-hoc cognitive networks where no central controller is available. In such networks, the players cannot communicate with each other and even may not know how many other players are present in the network. If multiple players select a common channel they collide, which results in loss of throughput for the colliding players. We consider both the static and dynamic scenarios where the number of players remains fixed throughout the game in the former case and can change in the later. We provide algorithms based on a novel 'trekking approach' that guarantees with high probability constant regret for the static case and sub-linear regret for the dynamic case. The trekking approach gives improved aggregate throughput and also results in fewer collisions compared to the state-of-the-art algorithms.
References
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A. Anandkumar, N. Michael, A. K. Tang, and A. Swami. 2011. Distributed algorithms for learning and cognitive medium access with logarithmic regret. IEEE Journal on Selected Areas in Communications 29, 4 (2011), 731--745.
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O. Avner and S. Mannor. 2014. Concurrent bandits and cognitive radio networks. In Proceedings of the Ma- chine Learning and Knowledge Discovery in Databases. Springer, Stanford, CA.
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Lilian Besson and Emilie Kaufmann. 2018. Multi-Player Bandits Models Revisited. In To appear in Algorithmic Learning Theory (ALT).
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M. K. Hanawal and S. J. Darak. {n. d.}. Multi-Player Bandits: A Trekking Approach. https://arxiv.org/pdf/1809.06040.pdf.
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D. Kalathil, N. Nayyar, and R. Jain. 2014. Decentralized learning for Multiplayer Multiarmed bandits. IEEE Transactions on Information Theory 60, 4 (2014), 2331--2345.
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G. Lugosi and A. Mehrabian. {n. d.}. Multiplayer bandits without observing collision information. https://arxiv.org/abs/1808.08416.
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M. Ozger, F. Alagoz, and O. B. Akan. 2018. Clustering in Multi-Channel Cognitive Radio Ad Hoc and Sensor Networks. IEEE Communication Magazine 56, 4 (2018), 156--162.
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Association for Computing Machinery
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Publication History
Published: 25 January 2019
Published in SIGMETRICS Volume 46, Issue 3
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View all- Hanawal MDarak S(2022)Multiplayer Bandits: A Trekking ApproachIEEE Transactions on Automatic Control10.1109/TAC.2021.307745467:5(2237-2252)Online publication date: May-2022
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