Hardware-Efficient Cognitive Radar: Multi-Target Detection with RL-Driven Transmissive RIS
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912590788034560 |
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| author | Umra, Adam Ahmed, Aya Mostafa Roth, Stefan Sezgin, Aydin |
| author_facet | Umra, Adam Ahmed, Aya Mostafa Roth, Stefan Sezgin, Aydin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14160 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hardware-Efficient Cognitive Radar: Multi-Target Detection with RL-Driven Transmissive RIS Umra, Adam Ahmed, Aya Mostafa Roth, Stefan Sezgin, Aydin Signal Processing 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. |
| title | Hardware-Efficient Cognitive Radar: Multi-Target Detection with RL-Driven Transmissive RIS |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2509.14160 |