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

TL;DR

This paper introduces an aerial RIS-assisted ISAC system utilizing deep reinforcement learning to optimize communication and sensing in battlefield scenarios, improving situational awareness by enhancing signal quality and target detection.

Contribution

It proposes a novel aerial RIS-assisted ISAC system with joint optimization via DRL, including beamforming, RIS phase shifts, and ARIS trajectory, for improved battlefield awareness.

Findings

Outperforms benchmark schemes in suppressing interference and clutter.

Optimizes RIS phase shifts and ARIS trajectory for better sensing and communication.

Demonstrates significant performance gains in numerical simulations.

Abstract

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.