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

TL;DR

This paper introduces a dual UAV cluster-assisted system using collaborative beamforming to enhance physical layer security in maritime communications, optimizing signal quality, security, and energy efficiency through advanced swarm intelligence algorithms.

Contribution

It proposes a novel dual UAV cluster system with collaborative beamforming for secure maritime wireless communications, including a new multi-objective optimization framework and an improved solution algorithm.

Findings

The proposed algorithm outperforms other methods in simulations.

The system effectively enhances signal strength and security.

Energy consumption is minimized while maintaining communication quality.

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.