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- W4297882632 abstract "We propose and evaluate a learning-based framework to address multi-agent resource allocation in coupled wireless systems. In particular we consider, multiple agents (e.g., base stations, access points, etc.) that choose amongst a set of resource allocation options towards achieving their own performance objective /requirements, and where the performance observed at each agent is further coupled with the actions chosen by the other agents, e.g., through interference, channel leakage, etc. The challenge is to find the best collective action. To that end we propose a Multi-Armed Bandit (MAB) framework wherein the best actions (aka arms) are adaptively learned through online reward feedback. Our focus is on systems which are weakly-coupled wherein the best arm of each agent is invariant to others' arm selection the majority of the time - this majority structure enables one to develop light weight efficient algorithms. This structure is commonly found in many wireless settings such as channel selection and power control. We develop a bandit algorithm based on the Track-and-Stop strategy, which shows a logarithmic regret with respect to a genie. Finally through simulation, we exhibit the potential use of our model and algorithm in several wireless application scenarios." @default.
- W4297882632 created "2022-10-01" @default.
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- W4297882632 date "2022-10-03" @default.
- W4297882632 modified "2023-10-04" @default.
- W4297882632 title "Online learning for multi-agent based resource allocation in weakly coupled wireless systems" @default.
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- W4297882632 doi "https://doi.org/10.1145/3492866.3549714" @default.
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