Trajectory Design for UAV-Enabled Maritime Secure Communications:A Reinforcement Learning Approach  

作　　者：Jintao Liu Feng Zeng Wei Wang Zhichao Sheng Xinchen Wei Kanapathippillai Cumanan 

机构地区：[1]School of Information Science and Technology,Nantong University,Nantong 226019,China [2]Nantong Research Institute for Advanced Communication Technologies,Nantong 226019,China [3]Key Laboratory of Specialty Fiber Optics and Optical Access Networks,Shanghai University,Shanghai 200444,China [4]Department of Electronic Engineering,University of York,York,YO105DD,United Kingdom

出　　处：《China Communications》2022年第9期26-36,共11页中国通信（英文版）

基　　金：supported by the Six Categories Talent Peak of Jiangsu Province(No.KTHY-039);the Future Network Scientific Research Fund Project(No.FNSRFP-2021-YB-42);the Science and Technology Program of Nantong(No.JC2021016);the Key Research and Development Program of Jiangsu Province of China(No.BE2021013-1)。

摘　　要：This paper investigates an unmanned aerial vehicle(UAV)-enabled maritime secure communication network,where the UAV aims to provide the communication service to a legitimate mobile vessel in the presence of multiple eavesdroppers.In this maritime communication networks(MCNs),it is challenging for the UAV to determine its trajectory on the ocean,since it cannot land or replenish energy on the sea surface,the trajectory should be pre-designed before the UAV takes off.Furthermore,the take-off location of the UAV and the sea lane of the vessel may be random,which leads to a highly dynamic environment.To address these issues,we propose two reinforcement learning schemes,Q-learning and deep deterministic policy gradient(DDPG)algorithms,to solve the discrete and continuous UAV trajectory design problem,respectively.Simulation results are provided to validate the effectiveness and superior performance of the proposed reinforcement learning schemes versus the existing schemes in the literature.Additionally,the proposed DDPG algorithm converges faster and achieves higher utilities for the UAV,compared to the Q-learning algorithm.

关 键 词：maritime communication networks(MCNs) unmanned aerial vehicles(UAV) reinforcement learning physical layer security trajectory design 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TN918[自动化与计算机技术—控制科学与工程] V19[电子电信—通信与信息系统]