Reinforcement Learning for Energy-Efficient User Association in UAV-Assisted Cellular Networks-Reference-Cited by-同舟云学术

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Reinforcement Learning for Energy-Efficient User Association in UAV-Assisted Cellular Networks

Published:2024-04 Issue:2 Volume:60 Page:2474-2481
ISSN:0018-9251
Container-title:IEEE Transactions on Aerospace and Electronic Systems
language:
Short-container-title:IEEE Trans. Aerosp. Electron. Syst.

Author:

Kaleem Zeeshan¹^ORCID,Khalid Waqas²^ORCID,Ahmad Ayaz¹^ORCID,Yu Heejung³^ORCID,Almasoud Abdullah M.⁴^ORCID,Yuen Chau⁵^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan

2. Institute of Industrial Technology, Korea University, Sejong, South Korea

3. Department of Electronics and Information Engineering, Korea University, Sejong, South Korea

4. Department of Electrical Engineering, Prince Sattam bin Abdulaziz University, Al Kharj, Saudi Arabia

5. School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore

Funder

Ministry of Science and ICT, Korea

Institute for Information and Communications Technology Planning and Evaluation

National Research Foundation of Korea

Korea University

Higher Education Commission Pakistan

Prince Sattam bin Abdulaziz University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7/10496926/10409538.pdf?arnumber=10409538

Reference25 articles.

1. Machine-Learning-Based UAV-Assisted Agricultural Information Security Architecture and Intrusion Detection

2. User association and power allocation for UAV-assisted networks: A distributed reinforcement learning approach

3. Role of UAVs in Public Safety Communications: Energy Efficiency Perspective

4. Joint Power and 3D Trajectory Optimization for UAV-Enabled Wireless Powered Communication Networks With Obstacles

5. SecBoost: Secrecy-Aware Deep Reinforcement Learning Based Energy-Efficient Scheme for 5G HetNets

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Graph-Attention-Based Reinforcement Learning for Trajectory Design and Resource Assignment in Multi-UAV-Assisted Communication;IEEE Internet of Things Journal;2024-08-15

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