AI 中文总结
本研究针对双无人机辅助的安全ISAC系统，提出迭代资源分配算法，通过分解优化问题并采用逐次凸逼近与块坐标下降技术，提升系统平均保密速率，算法经数值验证有效收敛。
AI 中文摘要
集成感知与通信（ISAC）是下一代无线通信网络中的新兴技术，可通过充分利用有限频谱资源同时执行通信与感知任务。本研究探讨双无人机（UAV）辅助的安全ISAC系统的保密性能：具体而言，基站无人机与用户通信并发射雷达信号定位潜在窃听者，同时向干扰无人机提供信息以执行干扰任务。考虑无人机最大速度、发射功率、推进能量及感知阈值等约束，我们通过优化用户调度策略、时间分配、发射功率及无人机轨迹，最大化平均保密速率。由于变量紧密耦合导致非凸问题，我们采用高效迭代算法求解：将原优化问题分解为6个子问题，通过逐次凸逼近将非凸子问题转化为近似凸形式，再采用块坐标下降技术依次求解所有子问题。数值结果验证了所提算法的收敛性与有效性。
英文摘要
Integrated sensing and communication (ISAC) is a rising technology in the next wireless communication networks, enabling the simultaneous execution of communication and sensing tasks by fully utilizing limited spectrum resources. In this work, we investigate the secrecy performance of a dual-uncrewed aerial vehicle (UAV)-assisted secure ISAC system. Specifically, a base station UAV communicates with users and transmits radar signals to locate potential eavesdroppers, while simultaneously providing information to a jammer UAV to perform jamming tasks. Considering constraints such as maximum UAV velocity, transmit power, propulsion energy, and sensing thresholds, we maximize the average secrecy rate by optimizing user scheduling strategies, time allocation, transmit power, and UAV trajectories. The presence of a non-convex problem, originating from tightly coupled variables, is tackled by an efficient iterative algorithm. In particular, the original optimization problem is decomposed into six subproblems, and non-convex subproblems are transformed into approximately convex forms via successive convex approximation. Then, block coordinate descent techniques are employed to solve all subproblems sequentially. Numerical results demonstrate the convergence and effectiveness of the proposed algorithm.
Comments13 pages,6 figures, submitted to Digital Communications and Networks to review
