Caching for Doubly Selective Fading Channels via Model-Agnostic Meta-Reinforcement Learning-Reference-Cited by-同舟云学术

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Caching for Doubly Selective Fading Channels via Model-Agnostic Meta-Reinforcement Learning

Published:2024-09 Issue:3 Volume:18 Page:1776-1785
ISSN:1932-8184
Container-title:IEEE Systems Journal
language:
Short-container-title:IEEE Systems Journal

Author:

He Weibao¹,Zhou Fasheng²^ORCID,Tang Dong²^ORCID,Fang Fang³^ORCID,Chen Wei⁴^ORCID

Affiliation:

1. School of Physics and Materials Science, Guangzhou University, Guangzhou, China

2. School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China

3. Department of Electrical and Computer Engineering and Department of Computer Science, Western University, London, ON, Canada

4. Department of Electronic Engineering, Tsinghua University, Beijing, China

Funder

National Natural Science Foundation of China

Science and Technology Program of Guangzhou

Key Laboratory of On-Chip Communication and Sensor Chip of Guangdong Higher Education Institutes

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/4267003/10678796/10643614.pdf?arnumber=10643614

Reference45 articles.

1. A Survey of Caching Techniques in Cellular Networks: Research Issues and Challenges in Content Placement and Delivery Strategies

2. Joint Optimization of Caching Placement and Trajectory for UAV-D2D Networks

3. Latency-Aware Radio Resource Optimization in Learning-Based Cloud-Aided Small Cell Wireless Networks

4. Deep Learning-enabled Joint Edge Content Caching and Power Allocation Strategy in Wireless Networks

5. Meta-reinforcement learning for edge caching in vehicular networks

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