Deep-Reinforcement-Learning-Based Resource Allocation for Content Distribution in Fog Radio Access Networks-Reference-Cited by-同舟云学术

Deep-Reinforcement-Learning-Based Resource Allocation for Content Distribution in Fog Radio Access Networks

Published:2022-09-15 Issue:18 Volume:9 Page:16874-16883
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Fang Chao¹^ORCID,Xu Hang¹,Yang Yihui¹,Hu Zhaoming¹^ORCID,Tu Shanshan¹^ORCID,Ota Kaoru²^ORCID,Yang Zheng³^ORCID,Dong Mianxiong²^ORCID,Han Zhu⁴^ORCID,Yu F. Richard⁵^ORCID,Liu Yunjie¹

Affiliation:

1. Faculty of Information Technology, Beijing University of Technology, Beijing, China

2. Department of Sciences and Informatics, Muroran Institute of Technology, Muroran, Japan

3. Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Normal University, Fuzhou, China

4. Department of Electrical and Computer Engineering, The University of Houston, Houston, TX, USA

5. School of Information Technology, Carleton University, Ottawa, Canada

Funder

Scientific Research Plan of Beijing Municipal Commission of Education

Beijing Nova Program of Science and Technology

Beijing Natural Science Foundation

National Science Foundation

Toyota and Amazon, JSPS KAKENHI

Leading Initiative for Excellent Young Researchers

MEXT, Japan

JST, PRESTO, Japan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/9881201/09691477.pdf?arnumber=9691477

Reference45 articles.

1. Deep Reinforcement Learning for Mobile Edge Caching: Review, New Features, and Open Issues

2. Smart Resource Allocation for Mobile Edge Computing: A Deep Reinforcement Learning Approach

3. Traffic-Aware Base Station Sleeping Control and Power Matching for Energy-Delay Tradeoffs in Green Cellular Networks

4. In-network caching effect on optimal energy consumption in content-centric networking

5. Web caching and Zipf-like distributions: evidence and implications

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