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Article Dans Une Revue IEEE Transactions on Aerospace and Electronic Systems Année : 2022

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

Résumé

In the present work, a reinforcement learning (RL) based adaptive algorithm to optimise the transmit beampattern for a co-located 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 sub-optimal, 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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Dates et versions

hal-03648448 , version 1 (21-04-2022)

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Francesco Lisi, Stefano Fortunati, Maria Sabrina Greco, Fulvio Gini. Enhancement of a state-ofthe-art RL-based detection algorithm for Massive MIMO radars. IEEE Transactions on Aerospace and Electronic Systems, 2022, ⟨10.1109/TAES.2022.3168033⟩. ⟨hal-03648448⟩
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