LGAIITMay 20, 2022

Fast Change Identification in Multi-Play Bandits and its Applications in Wireless Networks

arXiv:2205.10366v32 citationsh-index: 14
AI Analysis

This work addresses the need for efficient probing and change detection in wireless networks, offering a domain-specific incremental improvement over existing bandit algorithms.

The paper tackles the problem of identifying changes in reward distributions in non-stationary multi-armed bandits, particularly for wireless network applications, by developing TS-GE, which achieves regret guarantees that outperform state-of-the-art algorithms like ADSWITCH and M-UCB under certain conditions. It demonstrates efficacy in wireless tasks such as mobile-edge computing and industrial IoT networks.

Next-generation wireless services are characterized by a diverse set of requirements, to sustain which, the wireless access points need to probe the users in the network periodically. In this regard, we study a novel multi-armed bandit (MAB) setting that mandates probing all the arms periodically while keeping track of the best current arm in a non-stationary environment. In particular, we develop \texttt{TS-GE} that balances the regret guarantees of classical Thompson sampling (TS) with the broadcast probing (BP) of all the arms simultaneously in order to actively detect a change in the reward distributions. The main innovation in the algorithm is in identifying the changed arm by an optional subroutine called group exploration (GE) that scales as $\log_2(K)$ for a $K-$armed bandit setting. We characterize the probability of missed detection and the probability of false-alarm in terms of the environment parameters. We highlight the conditions in which the regret guarantee of \texttt{TS-GE} outperforms that of the state-of-the-art algorithms, in particular, \texttt{ADSWITCH} and \texttt{M-UCB}. We demonstrate the efficacy of \texttt{TS-GE} by employing it in two wireless system application - task offloading in mobile-edge computing (MEC) and an industrial internet-of-things (IIoT) network designed for simultaneous wireless information and power transfer (SWIPT).

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