Deep Reinforcement Learning for Jointly Resource Allocation and Trajectory Planning in UAV-Assisted Networks

被引：1
|
作者：
Jwaifel, Arwa Mahmoud [1 ]
Van Do, Tien [1 ]
机构：
[1] Budapest Univ Technol & Econ BME, Dept Networked Syst & Serv, Fac Elect Engn & Informat, Budapest, Hungary
来源：
COMPUTATIONAL COLLECTIVE INTELLIGENCE, ICCCI 2023 | 2023年 / 14162卷
关键词：
5G; 6G; Unmanned aerial vehicle (UAV); resource allocation optimization; deep reinforcement learning (DRL); Proximal Policy Optimization (PPO); Deep Reinforcement Learning (DQN); OPTIMIZATION; DESIGN;
D O I：
10.1007/978-3-031-41456-5_6
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Unmanned aerial vehicles (UAVs) have diverse applications in various fields, including the deployment of drones in 5G mobile networks and upcoming 6G and beyond. In UAV wireless networks, where the UAV is equipped with an eNB or gNB, it is critical to position it optimally to serve the maximum number of users located in high-capacity areas. Furthermore, the high mobility of users leads to greater network dynamics, making it challenging to predict channel link states. This study examines the use of Proximal Policy Optimization (PPO) to optimize the joint UAV position and radio spectrum resource allocation to meet the users' quality-of-service (QoS) requirements.
引用
收藏
页码：71 / 83
页数：13
相关论文
共 50 条
  • [1] Deep Reinforcement Learning Based Trajectory Design and Resource Allocation for UAV-Assisted Communications
    Zhang, Chiya
    Li, Zhukun
    He, Chunlong
    Wang, Kezhi
    Pan, Cunhua
    [J]. IEEE COMMUNICATIONS LETTERS, 2023, 27 (09) : 2398 - 2402
  • [2] Trajectory Planning in UAV-Assisted Wireless Networks via Reinforcement Learning
    He, Simeng
    Zhang, Shangwei
    [J]. 2022 IEEE 23RD INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE SWITCHING AND ROUTING (IEEE HPSR), 2022, : 232 - 237
  • [3] Resource Allocation in UAV-Assisted Wireless Networks Using Reinforcement Learning
    Luong, Phuong
    Gagnon, Francois
    Labeau, Fabrice
    [J]. 2020 IEEE 92ND VEHICULAR TECHNOLOGY CONFERENCE (VTC2020-FALL), 2020,
  • [4] Deep Reinforcement Learning-Empowered Trajectory and Resource Allocation Optimization for UAV-Assisted MEC Systems
    Sun, Haowen
    Chen, Ming
    Pan, Yijin
    Cang, Yihan
    Zhao, Jiahui
    Sun, Yuanzhi
    [J]. IEEE WIRELESS COMMUNICATIONS LETTERS, 2024, 13 (07) : 1823 - 1827
  • [5] Deep Reinforcement Learning-Based Resource Allocation in Cooperative UAV-Assisted Wireless Networks
    Luong, Phuong
    Gagnon, Francois
    Tran, Le-Nam
    Labeau, Fabrice
    [J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2021, 20 (11) : 7610 - 7625
  • [6] Deep Reinforcement Learning Based Resource Management in UAV-Assisted IoT Networks
    Munaye, Yirga Yayeh
    Juang, Rong-Terng
    Lin, Hsin-Piao
    Tarekegn, Getaneh Berie
    Lin, Ding-Bing
    [J]. APPLIED SCIENCES-BASEL, 2021, 11 (05): : 1 - 20
  • [7] Resource Allocation in UAV-Assisted Networks: A Clustering-Aided Reinforcement Learning Approach
    Zhou, Shiyang
    Cheng, Yufan
    Lei, Xia
    Peng, Qihang
    Wang, Jun
    Li, Shaoqian
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2022, 71 (11) : 12088 - 12103
  • [8] Resource allocation for UAV-assisted 5G mMTC slicing networks using deep reinforcement learning
    Rohit Kumar Gupta
    Saubhik Kumar
    Rajiv Misra
    [J]. Telecommunication Systems, 2023, 82 : 141 - 159
  • [9] Resource allocation for UAV-assisted 5G mMTC slicing networks using deep reinforcement learning
    Gupta, Rohit Kumar
    Kumar, Saubhik
    Misra, Rajiv
    [J]. TELECOMMUNICATION SYSTEMS, 2023, 82 (01) : 141 - 159
  • [10] Information freshness-oriented trajectory planning and resource allocation for UAV-assisted vehicular networks
    Gai, Hao
    Zhang, Haixia
    Guo, Shuaishuai
    Yuan, Dongfeng
    [J]. CHINA COMMUNICATIONS, 2023, 20 (05) : 244 - 262
12345