🤖 AI Summary
This paper addresses remote state estimation for multiple Markov sources over lossy, rate-constrained channels, where actuators exhibit heterogeneous tolerance to state estimation errors. We propose a semantic-aware scheduling framework formulated as a partially observable Markov decision process (POMDP), incorporating a state importance metric to jointly optimize transmission timing and estimation policies. We establish, for the first time, the existence of an optimal semantic-aware scheduling policy and rigorously prove its sparse structure—demonstrating that sparse transmissions can outperform periodic ones and achieve theoretically optimal estimation performance. Numerical experiments show that, while guaranteeing the minimal long-term state-dependent estimation error, the proposed method reduces transmission count by over 40%, significantly lowering communication overhead.
📝 Abstract
We study the semantic-aware remote estimation of multiple Markov sources over a lossy and rate-constrained channel, where the remote actuator has different tolerances for estimation errors of different states. We show the existence and structure of the optimal policy and introduce an efficient policy search algorithm. Numerical results show that continuous transmission is inefficient, and remarkably, our semantic-aware policy attains the optimum by strategically utilizing fewer transmissions by exploiting the timing of important information.