DRL-based Resource Allocation in Remote State Estimation

TL;DR

This paper develops deep reinforcement learning algorithms for optimizing resource allocation in remote state estimation over realistic wireless channels, improving scalability and estimation accuracy in complex multi-access scenarios.

Contribution

It introduces a comprehensive framework for resource allocation in remote estimation with practical wireless models, using advanced DRL techniques for large action spaces.

Findings

Algorithms outperform existing methods in scalability and estimation accuracy.

Proposed methods effectively handle large discrete and continuous action spaces.

Systematic analysis under different multiple-access schemes demonstrates robustness.

Abstract

Remote state estimation, where sensors send their measurements of distributed dynamic plants to a remote estimator over shared wireless resources, is essential for mission-critical applications of Industry 4.0. Existing algorithms on dynamic radio resource allocation for remote estimation systems assumed oversimplified wireless communications models and can only work for small-scale settings. In this work, we consider remote estimation systems with practical wireless models over the orthogonal multiple-access and non-orthogonal multiple-access schemes. We derive necessary and sufficient conditions under which remote estimation systems can be stabilized. The conditions are described in terms of the transmission power budget, channel statistics, and plants' parameters. For each multiple-access scheme, we formulate a novel dynamic resource allocation problem as a decision-making problem for achieving the minimum overall long-term average estimation mean-square error. Both the estimation quality and the channel quality states are taken into account for decision making. We systematically investigated the problems under different multiple-access schemes with large discrete, hybrid discrete-and-continuous, and continuous action spaces, respectively. We propose novel action-space compression methods and develop advanced deep reinforcement learning algorithms to solve the problems. Numerical results show that our algorithms solve the resource allocation problems effectively and provide much better scalability than the literature.