Optimality of Myopic Sensing in Multi-Channel Opportunistic Access

By Qing Zhao, Bhaskar Krishnamachari, Keqin Liu. IEEE International Conference on Communications, 2008

377 citations recorded by the source corpus.

How researchers see this paper

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Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, methodology): “As shown in the simulation results, the proposed scheme is close to the optimal approach [52] and outperforms the myopic policy [45] in terms of total rate.”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, background): “oadcast nature of the wireless channel environment and the access contention mechanism among multi-priority users, multi-armed bandit based techniques have played a special role in cognitive networks [272]– [277]. For example, Zhao et al. [272] formulated a multi-armed restless bandit model for oppo”
Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks (2018, background): “Researchers in [8] and [9] have studied its optimality when N channels are independent and statistically identical Gilbert-Elliot channels that follow the same 2-state Markov chain with the transition”
Indexability of Restless Bandit Problems and Optimality of Whittle Index for Dynamic Multichannel Access (2008, background): “This result, c oupled with recent findings in [10], [11] on the myopic policy for this class of RMBP, shows that Wh ittle’s index policy achieves the optimal performance under certain conditions and has a semi -universal structure that is robust against model mismatch and variations.”