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We consider optimal sensor scheduling with unknown communication channelstatistics. We formulate two types of scheduling problems with thecommunication rate being a soft or hard constraint, respectively. We firstpresent some structural results on the optimal scheduling policy using dynamicprogramming and assuming the channel statistics is known. We prove that theQ-factor is monotonic and submodular, which leads to the threshold-likestructures in both types of problems. Then we develop a stochasticapproximation and parameter learning frameworks to deal with the two schedulingproblems with unknown channel statistics. We utilize their structures to designspecialized learning algorithms. We prove the convergence of these algorithms.Performance improvement compared with the standard Q-learning algorithm isshown through numerical examples.

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