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arXiv · 2112.02628

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

Francesco Lisi·Stefano Fortunati·Maria Sabrina Greco·Fulvio Gini
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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.

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Francesco Lisi, Stefano Fortunati, Maria Sabrina Greco, Fulvio Gini. 2021-12-05. Enhancement of a state-of-the-art RL-based detection algorithm for Massive MIMO radars. https://doi.org/10.1109/taes.2022.3168033

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