Learning Optimal Scheduling Policy for Remote State Estimation under Uncertain Channel Condition
For control systems engineers, this work provides a method to learn scheduling policies without prior channel knowledge, but the improvement is incremental over existing Q-learning approaches.
This paper addresses optimal sensor scheduling under unknown communication channel statistics, proving structural properties of the optimal policy and developing specialized learning algorithms that outperform standard Q-learning in numerical examples.
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.