Multifunction Cognitive Radar Task Scheduling Using Monte Carlo Tree
  Search and Policy Networks
  A modern radar may be designed to perform multiple functions, such as
surveillance, tracking, and fire control. Each function requires the radar to
execute a number of transmit-receive tasks. A radar resource management (RRM)
module makes decisions on parameter selection, prioritization, and scheduling
of such tasks. RRM becomes especially challenging in overload situations, where
some tasks may need to be delayed or even dropped. In general, task scheduling
is an NP-hard problem. In this work, we develop the branch-and-bound (B&B)
method which obtains the optimal solution but at exponential computational
complexity. On the other hand, heuristic methods have low complexity but
provide relatively poor performance. We resort to machine learning-based
techniques to address this issue; specifically we propose an approximate
algorithm based on the Monte Carlo tree search method. Along with using bound
and dominance rules to eliminate nodes from the search tree, we use a policy
network to help to reduce the width of the search. Such a network can be
trained using solutions obtained by running the B&B method offline on problems
with feasible complexity. We show that the proposed method provides
near-optimal performance, but with computational complexity orders of magnitude
smaller than the B&B algorithm.
