Article: Trajectory planning for enhanced multi-agent deep deterministic policy gradient-based multi-UAV assisted maritime communication Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.9 pp.64 - 82 Abstract: A multi-UAV flight-path planning method is developed to provide communication services to user ships in blind maritime communication zones. The developed approach considers several limitations, such as the maximum flight speed and flight range. Owing to the limited energy and transmission range of UAVs, communication resources may be distributed unevenly, which could result in communication inequality. To address these problems, an optimisation problem is created to maximise the combined metrics of communication fairness and UAV energy efficiency. Considering the complexity of solving this optimisation problem, a deep-learning algorithm, DRL-1, is proposed. DRL-1 optimises the multi-UAV flight path-planning problem by utilising Ape-X and RNN based on the MADDPG method. Simulation result 1 demonstrates that the proposed optimisation algorithm effectively enhances the UAVs' energy efficiency and communication fairness. Simulation result 2 shows a significant improvement in UAVs' energy efficiency and communication fairness as the number of UAVs increases. Inderscience Publishers - linking academia, business and industry through research

Title: Trajectory planning for enhanced multi-agent deep deterministic policy gradient-based multi-UAV assisted maritime communication

Authors: Zhenyu Xu

Addresses: School of Information Engineering, Shanghai Maritime University, Shanghai, Shanghai, China

Abstract: A multi-UAV flight-path planning method is developed to provide communication services to user ships in blind maritime communication zones. The developed approach considers several limitations, such as the maximum flight speed and flight range. Owing to the limited energy and transmission range of UAVs, communication resources may be distributed unevenly, which could result in communication inequality. To address these problems, an optimisation problem is created to maximise the combined metrics of communication fairness and UAV energy efficiency. Considering the complexity of solving this optimisation problem, a deep-learning algorithm, DRL-1, is proposed. DRL-1 optimises the multi-UAV flight path-planning problem by utilising Ape-X and RNN based on the MADDPG method. Simulation result 1 demonstrates that the proposed optimisation algorithm effectively enhances the UAVs' energy efficiency and communication fairness. Simulation result 2 shows a significant improvement in UAVs' energy efficiency and communication fairness as the number of UAVs increases.

Keywords: maritime communication; unmanned aerial vehicle; UAV; trajectory planning; Markov decision process; MDP; deep learning; multi-agent deep deterministic policy gradient; DDPG.

DOI: 10.1504/IJICT.2025.145827

International Journal of Information and Communication Technology, 2025 Vol.26 No.9, pp.64 - 82

Received: 18 Dec 2024
Accepted: 28 Feb 2025
Published online: 28 Apr 2025 *

Title: Trajectory planning for enhanced multi-agent deep deterministic policy gradient-based multi-UAV assisted maritime communication

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