An Online Approach to Dynamic Channel Access and Transmission Scheduling
Yang Liu, Mingyan Liu

TL;DR
This paper develops online learning algorithms for dynamic channel access and scheduling in multichannel wireless systems, achieving near-optimal performance without prior knowledge of channel statistics.
Contribution
It introduces algorithms that track optimal dynamic policies, achieving strong regret bounds and outperforming traditional weak-regret learning methods.
Findings
Algorithms achieve sub-linear regret over time.
Outperforms standard weak-regret algorithms.
Effective in multiuser and single-user multichannel settings.
Abstract
Making judicious channel access and transmission scheduling decisions is essential for improving performance as well as energy and spectral efficiency in multichannel wireless systems. This problem has been a subject of extensive study in the past decade, and the resulting dynamic and opportunistic channel access schemes can bring potentially significant improvement over traditional schemes. However, a common and severe limitation of these dynamic schemes is that they almost always require some form of a priori knowledge of the channel statistics. A natural remedy is a learning framework, which has also been extensively studied in the same context, but a typical learning algorithm in this literature seeks only the best static policy, with performance measured by weak regret, rather than learning a good dynamic channel access policy. There is thus a clear disconnect between what an…
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Taxonomy
TopicsAdvanced Wireless Network Optimization · Advanced Bandit Algorithms Research · Advanced MIMO Systems Optimization
