Two-Way Aerial Secure Communications via Distributed Collaborative Beamforming under Eavesdropper Collusion
<|reference_start|>Two-Way Aerial Secure Communications via Distributed Collaborative Beamforming under Eavesdropper Collusion: Unmanned aerial vehicles (UAVs)-enabled aerial communication provides a flexible, reliable, and cost-effective solution for a range of wireless applications. However, due to the high line-of-sight (LoS) probability, aerial communications between UAVs are vulnerable to eavesdropping attacks, particularly when multiple eavesdroppers collude. In this work, we aim to introduce distributed collaborative beamforming (DCB) into UAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two UAV swarms and construct these swarms as two UAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and the maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of UAVs for constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives as a multi-objective optimization problem. Following this, we propose an enhanced multi-objective swarm intelligence algorithm via the characterized properties of the problem. Simulation results show that our proposed algorithm can obtain a set of informative solutions and outperform other state-of-the-art baseline algorithms. Experimental tests demonstrate that our method can be deployed in limited computing power platforms of UAVs and is beneficial for saving computational resources.<|reference_end|>
@article{li2024two-way,
    title={Two-Way Aerial Secure Communications via Distributed Collaborative
  Beamforming under Eavesdropper Collusion},
    author={Jiahui Li, Geng Sun, Qingqing Wu, Shuang Liang, Pengfei Wang, Dusit
  Niyato},
    journal={arXiv preprint arXiv:2404.07444},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07444},
    primaryClass={cs.NI eess.SP}
}