DRL-Based Resource Allocation in Remote State Estimation

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.

I. Introduction

Wireless networked control systems (WNCSs), consisting of spatially distributed plants, sensors, machines, actuators and controllers, play an essential role in the era of Industry 4.0 [2]. In particular, remote state estimators for monitoring dynamic plant status in a real-time manner are critical in WNCSs to enable high-quality closed-loop control. In Industry 4.0, massive wireless sensors are deployed for remote state estimation of spatially distributed plants. This raises fundamental questions: 1)

What are the primary conditions on wireless radio resources to enable stable remote estimation of all plants?

2)

How to manage the limited wireless radio resources for remote state estimation of many distributed plants to achieve the optimal estimation performance under different multiple-access schemes?