Enhancement of a State-of-the-Art RL-Based Detection Algorithm for Massive MIMO Radars
Francesco Lisi, Stefano Fortunati, Maria Greco, Fulvio Gini
Abstract
In the present article, a reinforcement learning (RL)-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptive</i> algorithm to optimize the transmit beampattern for a colocated massive multiple-input multiple-output (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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data-driven</i> scheme for the selection of the hyperparameters involved in the RL algorithm. Such an adaptive selection makes the Wald-RL-based detector independent of any <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> , and potentially suboptimal, manual tuning of the hyperparameters. Simulation results show the effectiveness of the proposed scheme in harsh scenarios with strong clutter and low SNR values.