{"ID":2869962,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.14160","arxiv_id":"2509.14160","title":"Hardware-Efficient Cognitive Radar: Multi-Target Detection with RL-Driven Transmissive RIS","abstract":"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.","short_abstract":"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....","url_abs":"https://arxiv.org/abs/2509.14160","url_pdf":"https://arxiv.org/pdf/2509.14160v1","authors":"[\"Adam Umra\",\"Aya Mostafa Ahmed\",\"Stefan Roth\",\"Aydin Sezgin\"]","published":"2025-09-17T16:43:26Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}