Caching Placement Optimization in UAV-Assisted Cellular Networks: A Deep Reinforcement Learning-Based Framework-Reference-Cited by-同舟云学术

Caching Placement Optimization in UAV-Assisted Cellular Networks: A Deep Reinforcement Learning-Based Framework

Published:2023-08 Issue:8 Volume:12 Page:1359-1363
ISSN:2162-2337
Container-title:IEEE Wireless Communications Letters
language:
Short-container-title:IEEE Wireless Commun. Lett.

Author:

Wang Yun¹^ORCID,Fu Shu¹^ORCID,Yao Changhua²^ORCID,Zhang Haijun³^ORCID,Yu Fei Richard⁴^ORCID

Affiliation:

1. College of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China

2. School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, China

3. Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services, University of Science and Technology Beijing, Beijing, China

4. Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/5962382/10213235/10121805.pdf?arnumber=10121805

Reference15 articles.

1. Optimal LAP Altitude for Maximum Coverage

2. Caching Placement and Resource Allocation for Cache-Enabling UAV NOMA Networks

3. A Systematic Review of Quality of Service in Wireless Sensor Networks using Machine Learning: Recent Trend and Future Vision

4. Non-Hermitian Symmetry Orthogonal Frequency Division Multiplexing for Multiple-Input Multiple-Output Visible Light Communications

5. Fresh, Fair and Energy-Efficient Content Provision in a Private and Cache-Enabled UAV Network

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