  Remote state estimation, where many 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. Most
of the existing works on remote state estimation assumed orthogonal multiple
access and the proposed dynamic radio resource allocation algorithms can only
work for very small-scale settings. In this work, we consider a remote
estimation system with non-orthogonal multiple access. We formulate a novel
dynamic resource allocation problem for achieving the minimum overall long-term
average estimation mean-square error. Both the estimation quality state and the
channel quality state are taken into account for decision making at each time.
The problem has a large hybrid discrete and continuous action space for joint
channel assignment and power allocation. We propose a novel action-space
compression method and develop an advanced deep reinforcement learning
algorithm to solve the problem. Numerical results show that our algorithm
solves the resource allocation problem effectively, presents much better
scalability than the literature, and provides significant performance gain
compared to some benchmarks.
