In this article, we consider the optimal sensor scheduling for remote state estimation in cyber–physical systems (CPSs). Different from the existing works concerning the time-invariant channel state in the wireless communication network, our work considers the time-varying channel state modeled by a finite-state Markov channel (FSMC). We focus on the problem of how to schedule the transmission of the sensor to minimize the estimation error at the remote side with less communication cost. Using the framework of the Markov decision process (MDP), the optimal scheduling policy is shown to be deterministic stationary (DS). We further derive its double threshold structure with respect to remote estimation errors and channel states. Moreover, a necessary and sufficient condition guaranteeing the mean-square stability of the remote estimator is given based on the structured scheduling policy. Numerical simulations are provided to verify the theoretical results.

中文翻译：

---

有限状态马尔可夫通道上传感器调度策略的双阈值结构


在本文中，我们考虑网络物理系统（CPS）中远程状态估计的最佳传感器调度。与无线通信网络中时不变信道状态的现有工作不同，我们的工作考虑由有限状态马尔可夫信道（FSMC）建模的时变信道状态。我们关注的问题是如何调度传感器的传输，以较小的通信成本最小化远程端的估计误差。使用马尔可夫决策过程（MDP）的框架，最优调度策略被证明是确定性平稳的（DS）。我们进一步推导了其关于远程估计误差和信道状态的双阈值结构。此外，基于结构化调度策略，给出了保证远程估计器均方稳定性的充要条件。提供数值模拟来验证理论结果。