Learning Optimal Scheduling Policy for Remote State Estimation under Uncertain Channel Condition

TL;DR

This paper develops optimal sensor scheduling policies for remote state estimation over uncertain channels, introducing learning algorithms that adapt to unknown channel conditions and outperform standard methods.

Contribution

It proposes structural results and specialized learning algorithms for sensor scheduling with unknown channel statistics, ensuring convergence and improved performance.

Findings

Structural threshold-like policies derived from monotonicity and submodularity.

Development of convergence-guaranteed learning algorithms.

Numerical results show performance improvements over standard Q-learning.

Abstract

We consider optimal sensor scheduling with unknown communication channel statistics. We formulate two types of scheduling problems with the communication rate being a soft or hard constraint, respectively. We first present some structural results on the optimal scheduling policy using dynamic programming and assuming the channel statistics is known. We prove that the Q-factor is monotonic and submodular, which leads to the threshold-like structures in both types of problems. Then we develop a stochastic approximation and parameter learning frameworks to deal with the two scheduling problems with unknown channel statistics. We utilize their structures to design specialized learning algorithms. We prove the convergence of these algorithms. Performance improvement compared with the standard Q-learning algorithm is shown through numerical examples.