In this work, we aim to introduce distributed collaborative beamforming (DCB) into AAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two AAV swarms and construct these swarms as two AAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of AAVs when constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives by formulating a multi-objective optimization problem, which is NP-hard and with a large number of decision variables. Accordingly, we design a novel generative swarm intelligence (GenSI) framework to solve the problem with less overhead, which contains a conditional variational autoencoder (CVAE)-based generative method and a proposed powerful swarm intelligence algorithm. In this framework, CVAE can collect expert solutions obtained by the swarm intelligence algorithm in other environment states to explore characteristics and patterns, thereby directly generating high-quality initial solutions in new environment factors for the swarm intelligence algorithm to search solution space efficiently. Simulation results show that the proposed swarm intelligence algorithm outperforms other state-of-the-art baseline algorithms, and the GenSI can achieve similar optimization results by using far fewer iterations than the ordinary swarm intelligence algorithm. Experimental tests demonstrate that introducing the CVAE mechanism achieves a 58.7% reduction in execution time, which enables the deployment of GenSI even on AAV platforms with limited computing power.

翻译：本文旨在将分布式协同波束成形技术引入AAV集群，并通过控制相应的信号分布来应对窃听者合谋问题。具体而言，我们考虑在两个支持双向DCB的AAV集群间建立空中通信，并将这些集群构建为两个AAV虚拟天线阵列。随后，我们通过最小化双向已知保密容量与最大旁瓣电平，分别避免已知与未知窃听者的信息泄露。同时，在构建虚拟天线阵列的过程中，我们也致力于最小化AAV的能耗。由于安全性能与能源效率之间存在相互制约的关系，我们将这些目标建模为一个多目标优化问题，该问题属于NP难问题且决策变量规模庞大。为此，我们设计了一种新颖的生成式群体智能框架，以较低开销求解该问题。该框架包含基于条件变分自编码器的生成方法以及一种新提出的高效群体智能算法。在此框架中，CVAE能够收集群体智能算法在其他环境状态下获得的专家解，以探索其特征与规律，从而直接针对新环境因素生成高质量初始解，供群体智能算法高效搜索解空间。仿真结果表明，所提出的群体智能算法性能优于其他先进基线算法，且GenSI框架仅需远少于常规群体智能算法的迭代次数即可达到相近的优化效果。实验测试表明，引入CVAE机制可使执行时间降低58.7%，这使得GenSI即使在计算能力有限的AAV平台上也能实现部署。