Cognitive radar has emerged as a key paradigm for next-generation sensing, enabling adaptive, intelligent operation in dynamic and complex environments. Yet, conventional cognitive multiple-input multiple-output (MIMO) radars offer strong detection performance but suffer from high hardware complexity and power demands. To overcome these limitations, we develop a reinforcement learning (RL)-based framework that leverages a transmissive reconfigurable intelligent surface (TRIS) for adaptive beamforming. A state-action-reward-state-action (SARSA) agent tunes TRIS phase shifts to improve multi-target detection in low signal-to-noise ratio (SNR) conditions while operating with far fewer radio frequency (RF) chains. Simulations confirm that the proposed TRIS-RL radar matches or, for large number of elements, even surpasses MIMO performance with reduced cost and energy requirements.
@article{arxiv.2509.14160,
title = {Hardware-Efficient Cognitive Radar: Multi-Target Detection with RL-Driven Transmissive RIS},
author = {Adam Umra and Aya Mostafa Ahmed and Stefan Roth and Aydin Sezgin},
journal= {arXiv preprint arXiv:2509.14160},
year = {2025}
}