Low-altitude wireless networks (LAWNs) represent a promising architecture that integrates unmanned aerial vehicles (UAVs) as aerial nodes to provide enhanced coverage, reliability, and throughput for diverse applications. However, these networks face significant security vulnerabilities from both known and potential unknown eavesdroppers, which may threaten data confidentiality and system integrity. To solve this critical issue, we propose a novel secure communication framework for LAWNs where the selected UAVs within a swarm function as a virtual antenna array (VAA), complemented by intelligent reflecting surface (IRS) to create a robust defense against eavesdropping attacks. Specifically, we formulate a multi-objective optimization problem that simultaneously maximizes the secrecy rate while minimizing the maximum sidelobe level and total energy consumption, requiring joint optimization of UAV excitation current weights, flight trajectories, and IRS phase shifts. This problem presents significant difficulties due to the dynamic nature of the system and heterogeneous components. Thus, we first transform the problem into a heterogeneous Markov decision process (MDP). Then, we propose a heterogeneous multi-agent control approach (HMCA) that integrates a dedicated IRS control policy with a multi-agent soft actor-critic framework for UAV control, which enables coordinated operation across heterogeneous network elements. Simulation results show that the proposed HMCA achieves superior performance compared to baseline approaches in terms of secrecy rate improvement, sidelobe suppression, and energy efficiency. Furthermore, we find that the collaborative and passive beamforming synergy between VAA and IRS creates robust security guarantees when the number of UAVs increases.

翻译：低空无线网络（LAWNs）作为一种前景广阔的架构，通过集成无人机作为空中节点，为多样化应用提供增强的覆盖范围、可靠性和吞吐量。然而，这些网络面临来自已知及潜在未知窃听者的严重安全漏洞，可能威胁数据机密性与系统完整性。为解决这一关键问题，我们提出一种新颖的LAWN安全通信框架：在集群内选取的无人机作为虚拟天线阵列（VAA），并辅以智能反射面（IRS）构建针对窃听攻击的鲁棒防御体系。具体而言，我们构建了一个多目标优化问题，在最大化保密速率的同时，最小化最大旁瓣电平与总能耗，这需要联合优化无人机激励电流权重、飞行轨迹及IRS相移。该问题因系统的动态特性和异构组件而存在显著求解难度。为此，我们首先将问题转化为异构马尔可夫决策过程（MDP），随后提出一种异构多智能体控制方法（HMCA），该方法将专用IRS控制策略与面向无人机控制的多智能体柔性执行者-评论家框架相结合，实现了异构网络元素间的协同操作。仿真结果表明，相较于基线方法，所提出的HMCA在保密速率提升、旁瓣抑制和能量效率方面均表现出更优性能。此外，我们发现当无人机数量增加时，VAA与IRS之间的协同被动波束赋形能产生鲁棒的安全保障。