Reinforcement learning based transmitter-receiver selection for distributed MIMO radars

Active transmitter-receiver (TX-RX) subset selection facilitates efficient resource use and adaptation to varying target and propagation environments in distributed multiple-input multiple-output (MIMO) radar systems. The problem has been addressed in the literature and objective functions related to radar tasks that depend on the signal-to-interference-plus-noise ratio (SINR) have been proposed. The SINR values observed at the receivers can be estimated assuming a particular propagation environment and target models. In this paper, a novel machine learning approach is proposed in which no such assumptions are needed. We formulate the TX-RX subset selection as a multi-armed bandit (MAB) problem and further extend it to the combinatorial MAB framework. A variety of reinforcement learning algorithms developed for the MAB problem are employed to learn the optimal subset in real-time. It is shown that such algorithms can be effectively used for the TX-RX subset selection problem even in non-stationary scenarios.