Real-Time Reconstruction and Actuation Error Analysis for Markov Sources over MPR Channels

TL;DR

This paper analyzes real-time reconstruction and actuation errors for Markov sources over MPR channels, deriving formulas and optimizing sampling strategies to improve performance.

Contribution

It provides closed-form expressions linking source dynamics and MPR models to reconstruction and actuation errors, enabling optimized sampling policies.

Findings

Optimized randomized sampling outperforms baseline strategies.

Derived explicit formulas for steady-state errors based on source and channel parameters.

Characterized how source dynamics and MPR coupling influence sampling resource allocation.

Abstract

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.