论文标题
稳定性强制强盗算法在远程状态的高斯 - 马尔科夫过程中进行渠道选择
Stability Enforced Bandit Algorithms for Channel Selection in Remote State Estimation of Gauss-Markov Processes
论文作者
论文摘要
在本文中，我们考虑了对高斯 - 马尔科夫过程的远程状态估计问题的问题，在每个离散时间瞬间，传感器可以从不同的通信渠道中传输一个。手头情况的一个关键困难是频道统计数据未知。我们研究了同时进行通道接收概率和状态估计的学习情况。提出了基于多武器匪徒技术选择通道的方法，并证明可以提供稳定性。此外，我们定义了估计遗憾的绩效概念，并在其如何随时间缩放的时间来得出了界限。
In this paper 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 is 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.
