  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.
