Dual UAV Cluster-Assisted Maritime Physical Layer Secure Communications via Collaborative Beamforming

• URL: http://arxiv.org/abs/2412.05949v1
• Date: Sun, 08 Dec 2024 14:11:02 GMT
• Title: Dual UAV Cluster-Assisted Maritime Physical Layer Secure Communications via Collaborative Beamforming
• Authors: Jiawei Huang, Aimin Wang, Geng Sun, Jiahui Li, Jiacheng Wang, Hongyang Du, Dusit Niyato,
• Abstract summary: Unmanned aerial vehicles (UAVs) can be utilized as relay platforms to assist maritime wireless communications. Collaborative beamforming (CB) can enhance the signal strength and range to assist the UAV relay for remote maritime communications. This paper proposes a dual UAV cluster-assisted system via CB to achieve physical layer security in maritime wireless communications.
• Score: 47.191944685913036
• License:
• Abstract: Unmanned aerial vehicles (UAVs) can be utilized as relay platforms to assist maritime wireless communications. However, complex channels and multipath effects at sea can adversely affect the quality of UAV transmitted signals. Collaborative beamforming (CB) can enhance the signal strength and range to assist the UAV relay for remote maritime communications. However, due to the open nature of UAV channels, security issue requires special consideration. This paper proposes a dual UAV cluster-assisted system via CB to achieve physical layer security in maritime wireless communications. Specifically, one UAV cluster forms a maritime UAV-enabled virtual antenna array (MUVAA) relay to forward data signals to the remote legitimate vessel, and the other UAV cluster forms an MUVAA jammer to send jamming signals to the remote eavesdropper. In this system, we formulate a secure and energy-efficient maritime communication multi-objective optimization problem (SEMCMOP) to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the total flight energy consumption of UAVs. Since the SEMCMOP is an NP-hard and large-scale optimization problem, we propose an improved swarm intelligence optimization algorithm with chaotic solution initialization and hybrid solution update strategies to solve the problem. Simulation results indicate that the proposed algorithm outperforms other comparison algorithms, and it can achieve more efficient signal transmission by using the CB-based method.

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