Stability Enforced Bandit Algorithms for Channel Selection in Remote State Estimation of Gauss-Markov Processes

• 成果类型: Article
• 署名作者: Leong, Alex S.; Quevedo, Daniel E.; Liu, Wanchun
• 署名单位: Defence Science & Technology; Queensland University of Technology (QUT); University of Sydney
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3299553
• 发表日期: 2023
• 页码: 8308-8315
• 关键词: Learning; multi-armed bandits; regret; STABILITY; State estimation

摘要

In this article, we consider the problem of remote state estimation of a Gauss-Markov process, where a sensor can, at each discrete time instant, transmit on one out of M different communication channels. A key difficulty of the situation at hand is that the channel statistics are unknown. We study the case where both learning of the channel reception probabilities and state estimation are carried out simultaneously. Methods for choosing the channels based on techniques for multi-armed bandits are presented, and shown to provide stability. Furthermore, we define the performance notion of estimation regret, and derive bounds on how it scales with time for the considered algorithms.