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

arXiv: https://arxiv.org/abs/2112.02628 · HTML: https://mlsanity.com/paper/2112.02628/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2021.

## Built on by (most cited first, 2 of 2)

- [POMDP-Driven Cognitive Massive MIMO Radar: Joint Target Detection-Tracking In Unknown Disturbances](https://mlsanity.com/paper/2410.17967/) (2024)
- [RL-Aided Cognitive ISAC: Robust Detection and Sensing-Communication Trade-offs](https://mlsanity.com/paper/2511.02672/) (2025)

## Abstract (arXiv)

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.