Deep Reinforcement Learning Based Trajectory Design and Resource Allocation for UAV-Assisted Communications

被引：17

作者：

Zhang, Chiya ^{[1
]}

Li, Zhukun ^{[1
]}

He, Chunlong ^{[2
]}

Wang, Kezhi ^{[3
]}

Pan, Cunhua ^{[4
]}

机构：

[1] Harbin Inst Technol, Sch Elect & Informat Engn, Shenzhen 518055, Peoples R China

[2] Shenzhen Univ, Guangdong Key Lab Intelligent Informat Proc, Shenzhen 518060, Peoples R China

[3] Brunel Univ London, Dept Comp Sci, London UB8 3PH, England

[4] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 211189, Peoples R China

来源：

IEEE COMMUNICATIONS LETTERS | 2023年 / 27卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Unmanned aerial vehicles; deep reinforcement learning; 3-D trajectory design; uncertain flight time;

D O I：

10.1109/LCOMM.2023.3292816

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

In this letter, we investigate the Unmanned Aerial Vehicles (UAVs)-assisted communications in three dimensional (3-D) environment, where one UAV is deployed to serve multiple user equipments (UEs). The locations and quality of service (QoS) requirement of the UEs are varying and the flying time of the UAV is unknown which depends on the battery of the UAVs. To address the issue, a proximal policy optimization 2 (PPO2)-based deep reinforcement learning (DRL) algorithm is proposed, which can control the UAV in an online manner. Specifically, it can allow the UAV to adjust its speed, direction and altitude so as to minimize the serving time of the UAV while satisfying the QoS requirement of the UEs. Simulation results are provided to demonstrate the effectiveness of the proposed framework.

引用

页码：2398 / 2402

页数：5

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