发表机构
Xi’an Jiaotong University; Shanghai Jiao Tong University; Beijing University of Posts and Telecommunications（西安交通大学; 上海交通大学; 北京邮电大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于模型预测控制的空中RIS辅助安全ISAC框架，联合优化无人机轨迹、波束成形与RIS相移，以最大化保密速率并最小化飞行能耗，仿真验证其优于静态基线。
AI 中文摘要
本文提出了一种新颖的空中可重构智能表面（RIS）辅助的安全集成感知与通信（ISAC）架构。一架搭载RIS的无人机（UAV）建立虚拟视距链路以绕过障碍物，在为合法用户服务的同时感知潜在窃听者。为确保通信安全与感知可靠性，我们构建了一个无限时域动态控制问题，以最大化长期平均系统保密速率并最小化无人机飞行能量。该问题联合优化无人机轨迹、基站处带人工噪声（AN）的主动波束成形以及RIS相移，并受到严格的雷达信噪比和无人机运动学约束。由于高度耦合的非凸变量，直接求解该问题难以处理。因此，我们提出了一种基于模型预测控制（MPC）的在线联合优化框架。在每个滚动时域内，采用交替优化将问题分解为三个子问题，并通过逐次凸逼近和差分凸规划高效求解。大量仿真表明，我们基于MPC的算法在保密速率上显著优于静态基线，展现出优越的在线校正能力和对环境扰动的鲁棒性。
英文摘要
This paper proposes a novel aerial reconfigurable intelligent surface (RIS)-aided secure integrated sensing and communication (ISAC) architecture. An unmanned aerial vehicle (UAV)-mounted RIS establishes virtual line-of-sight links to bypass blockages, serving legitimate users while sensing potential eavesdroppers. To ensure communication security and sensing reliability, we formulate an infinite-horizon dynamic control problem maximizing the long-term average system secrecy rate and minimizing UAV flight energy. This formulation jointly optimizes the UAV trajectory, active beamforming with artificial noise (AN) at the base station, and RIS phase shifts, subject to strict radar signal-to-noise ratio and UAV kinematic constraints. Due to the highly coupled non-convex variables, solving this problem directly is intractable. Therefore, we propose an online joint optimization framework based on model predictive control (MPC). Within each receding horizon, alternating optimization is employed to decouple the problem into three subproblems, efficiently solved via successive convex approximation and difference of convex programming. Extensive simulations demonstrate that our MPC-based algorithm significantly outperforms static baselines in secrecy rate, exhibiting superior online correction capability and robustness against environmental perturbations.
