Enhancement of a state-of-the-art RL-based detection algorithm for Massive MIMO radars

TL;DR

This paper introduces an adaptive reinforcement learning algorithm to optimize transmit beampatterns in massive MIMO radars, enhancing detection performance in challenging environments without manual hyper-parameter tuning.

Contribution

It develops a fully adaptive, data-driven hyper-parameter selection method for RL-based detection in massive MIMO radars, improving robustness and performance.

Findings

Effective in harsh clutter and low SNR scenarios

Reduces need for manual hyper-parameter tuning

Enhances detection robustness

Abstract

In the present work, a reinforcement learning (RL) based adaptive algorithm to optimise the transmit beampattern for a colocated massive MIMO radar is presented. Under the massive MIMO regime, a robust Wald type detector, able to guarantee certain detection performances under a wide range of practical disturbance models, has been recently proposed. Furthermore, an RL/cognitive methodology has been exploited to improve the detection performance by learning and interacting with the surrounding unknown environment. Building upon previous findings, we develop here a fully adaptive and data driven scheme for the selection of the hyper-parameters involved in the RL algorithm. Such an adaptive selection makes the Wald RL based detector independent of any ad hoc, and potentially suboptimal, manual tuning of the hyper-parameters. Simulation results show the effectiveness of the proposed scheme in harsh scenarios with strong clutter and low SNR values.