提升多无人机感知通信安全与效率的联合设计方法
Joint Hybrid Beamforming and Artificial Noise Design for Secure Multi-UAV ISAC Networks
- 分两阶段优化波束成形、干扰噪声和飞行轨迹
- 相比基准方案，总保密速率显著提升
- 适合研究无人机安全通信与智能调度的学者
感知与通信一体化（ISAC）是智能城市与自动驾驶等下一代应用的关键技术。将ISAC与无人机（UAV）结合，可在动态空域中实现可靠通信与精准感知。然而，现有研究多将无人机视为空中基站，忽视其作为ISAC用户的角色，且未充分利用地面基站的大规模天线阵列来提升安全性和频谱效率。本文提出一种面向多无人机网络的安全高效ISAC框架，采用两阶段优化方法联合设计混合波束成形（HBF）、人工噪声（AN）注入与无人机轨迹。第一阶段使用近端策略优化（PPO）优化数字波束成形器与轨迹，第二阶段通过低复杂度矩阵分解将数字解分解为模拟与数字组件。仿真结果表明，所提框架在总保密速率上优于基准方案。
原文摘要 · Abstract (English)
Integrated sensing and communication (ISAC) emerges as a key enabler for next-generation applications such as smart cities and autonomous systems. Its integration with unmanned aerial vehicles (UAVs) unlocks new potentials for reliable communication and precise sensing in dynamic aerial environments. However, existing research predominantly treats UAVs as aerial base stations, overlooking their role as ISAC users, and fails to leverage large-scale antenna arrays at terrestrial base stations to enhance security and spectral efficiency. This paper propose a secure and spectral efficient ISAC framework for multi-UAV networks, and a two-stage optimization approach is developed to jointly design hybrid beamforming (HBF), artificial noise (AN) injection, and UAV trajectories. Aiming at maximizing the sum secrecy rate, the first stage employs Proximal Policy Optimization (PPO) to optimize digital beamformers and trajectories, and the second stage decomposes the digital solution into analog and digital components via low-complexity matrix factorization. Simulation results demonstrate the effectiveness of the proposed framework compared to benchmark schemes.
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