发表机构
Department of Electrical and Computer Engineering（电气与计算机工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究无人机辅助的ISAC系统，在功率和移动性约束下联合优化无人机轨迹与波束成形参数以提升感知性能。采用零空间投影设计波束成形，深度强化学习优化轨迹，相比无无人机辅助系统，显著降低时间平均CRB，提高感知精度。
AI 中文摘要
本文研究了一种无人机辅助的综合感知与通信（ISAC）系统，其中无人机增强基站对目标的感知能力，同时确保对下行用户的可靠通信。由于无人机在无线环境中具有可控移动性和自适应感知覆盖，这种架构对未来无线网络具有实际吸引力。感知性能由平均克拉美罗界（CRB）表征，为增强感知性能，在功率和移动性约束下联合优化无人机轨迹和波束成形参数，同时满足对下行用户的通信要求。针对由此产生的非凸问题，采用零空间投影进行波束成形设计，并采用深度强化学习进行离散时间尺度的轨迹优化。仿真结果表明，与无无人机辅助的ISAC系统相比，该方法显著降低了时间平均CRB超过10%，并且比固定无人机轨迹和基于最大比传输的波束成形基准具有更高的感知精度。
英文摘要
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 Cramér-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.
Comments7 pages, accepted by 2026 IEEE 104th Vehicular Technology Conference (VTC2026-Fall)
