Real-Time Reconstruction and Actuation Error Analysis for Markov Sources over MPR Channels
Provides a tractable analysis and optimization framework for task-oriented sampling in wireless networks with MPR, relevant to real-time monitoring and control systems.
This paper derives closed-form expressions for real-time reconstruction and actuation errors for two Markov sources sharing an MPR channel, and shows that optimized randomized sampling outperforms baselines like random, greedy, and time-sharing.
We study real-time reconstruction and actuation for two binary Markov sources that share a wireless multi-packet reception (MPR) channel. Each sensor follows a stationary randomized sampling policy, and the receiver maintains source estimates using the most recently decoded updates. We derive closed-form expressions for the steady-state real-time reconstruction error (RTE) and the cost of actuation error (CAE) as functions of the source transition probabilities and the effective update probabilities. We then characterize these update probabilities under randomized sampling, linking the physical-layer MPR model to task-oriented reconstruction and actuation metrics. Using these expressions, we formulate a sampling-constrained optimization problem with a weighted-error objective. The resulting analysis reveals how source dynamics, semantic weights, and MPR coupling affect the allocation of sampling resources. Numerical results show that optimized randomized sampling outperforms random, greedy, and time-sharing baselines.