典型文献
Trajectory Design for UAV-Enabled Maritime Secure Communications:A Reinforcement Learning Approach
文献摘要：
This paper investigates an unmanned aerial vehicle (UAV)-enabled maritime secure com-munication 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 con-verges faster and achieves higher utilities for the UAV, compared to the Q-learning algorithm.
文献关键词：
中图分类号：
作者姓名：
Jintao Liu;Feng Zeng;Wei Wang;Zhichao Sheng;Xinchen Wei;Kanapathippillai Cumanan
作者机构：
School of Information Science and Technology,Nantong University,Nantong 226019,China;Nantong Research Institute for Advanced Communication Technologies,Nantong 226019,China;Key Laboratory of Specialty Fiber Optics and Optical Access Networks,Shanghai University,Shanghai 200444,China;Department of Electronic Engineering,University of York,York,YO105DD,United Kingdom
文献出处：
引用格式：
[1]Jintao Liu;Feng Zeng;Wei Wang;Zhichao Sheng;Xinchen Wei;Kanapathippillai Cumanan-.Trajectory Design for UAV-Enabled Maritime Secure Communications:A Reinforcement Learning Approach)[J].中国通信（英文版）,2022(09):26-36
A类：
MCNs,verges
B类：
Trajectory,Design,UAV,Enabled,Maritime,Secure,Communications,Reinforcement,Learning,Approach,This,paper,investigates,unmanned,aerial,vehicle,enabled,maritime,secure,where,aims,communication,service,legitimate,mobile,vessel,presence,multiple,eavesdroppers,In,this,networks,challenging,determine,its,trajectory,ocean,since,cannot,land,replenish,energy,sea,surface,should,designed,before,takes,off,Furthermore,location,lane,may,random,which,leads,highly,dynamic,environment,To,address,these,issues,we,reinforcement,learning,schemes,deep,deterministic,policy,gradient,DDPG,algorithms,solve,discrete,continuous,problem,respectively,Simulation,results,provided,validate,effectiveness,superior,performance,proposed,versus,existing,literature,Additionally,faster,achieves,higher,utilities,compared
AB值：
0.602065
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