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This paper explores a UAV-assisted integrated sensing and communication (ISAC) system, optimizing both UAV trajectory and beamforming parameters to enhance sensing performance while maintaining reliable communication. By employing null-space projection for beamforming and deep reinforcement learning for trajectory optimization, the authors tackle a non-convex optimization problem under power and mobility constraints. The results indicate a significant reduction in the average Cram茅r-Rao bound (CRB) by over 10% compared to traditional ISAC systems, demonstrating improved sensing accuracy over existing benchmarks.
UAV-assisted ISAC systems can achieve over 10% better sensing performance by optimizing trajectory and beamforming together, reshaping future wireless network designs.
In this paper, we study an unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) system, where a UAV enhances the sensing capability of a base station (BS) towards a target while ensuring reliable communication towards a downlink user. This architecture is practically attractive for future wireless networks due to the UAV's controllable mobility and adaptive sensing coverage in wireless environments. The sensing performance is characterized by the average Cram\'er-Rao bound (CRB), which quantifies the minimum variance of the unbiased angle-of-arrival estimation. To enhance the sensing performance, the UAV trajectory and beamforming parameters are jointly optimized under power and mobility constraints, while satisfying communication requirements to the downlink user. To address the resulting non-convex problem, we employ null-space projection for beamforming design and adopt deep reinforcement learning for the trajectory optimization over a discrete-time scale. In each time slot, beamforming is optimized based on the channel state information to improve CRB performance while mitigating interference between the BS and the communication user. Simulation results demonstrate that the proposed method significantly reduces the time-averaged CRB by over 10%, compared with the ISAC system without UAV assistance, and also achieves a higher sensing accuracy than both the fixed-UAV-trajectory and the maximum-ratio-transmission-based beamforming benchmarks.
