Enhancing Battlefield Awareness: An Aerial RIS-assisted ISAC System with Deep Reinforcement Learning

2024-05-30 · Hyunsang Cho, Seonghoon Yoo, Bang Chul Jung, Joonhyuk Kang

This paper considers a joint communication and sensing technique for enhancing situational awareness in practical battlefield scenarios. In particular, we propose an aerial reconfigurable intelligent surface (ARIS)-assisted integrated sensing and communication (ISAC) system consisting of a single access point (AP), an ARIS, multiple users, and a sensing target. With deep reinforcement learning (DRL), we jointly optimize the transmit beamforming of the AP, the RIS phase shifts, and the trajectory of the ARIS under signal-to-interference-noise ratio (SINR) constraints. Numerical results demonstrate that the proposed technique outperforms the conventional benchmark schemes by suppressing the self-interference and clutter echo signals or optimizing the RIS phase shifts.

📄 PDF Abstract BibTeX arXiv:2405.20168

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Deep Reinforcement LearningIntegrated sensing and communicationISAC

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