Enhancing Battlefield Awareness: An Aerial RIS-assisted ISAC System with Deep Reinforcement Learning
<|reference_start|>Enhancing Battlefield Awareness: An Aerial RIS-assisted ISAC System with Deep Reinforcement Learning: 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.<|reference_end|>
@article{cho2024enhancing,
    title={Enhancing Battlefield Awareness: An Aerial RIS-assisted ISAC System with
  Deep Reinforcement Learning},
    author={Hyunsang Cho, Seonghoon Yoo, Bang Chul Jung, Joonhyuk Kang},
    journal={arXiv preprint arXiv:2405.20168},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20168},
    primaryClass={eess.SY cs.SY}
}