Novel Edge Caching Approach Based on Multi-Agent Deep Reinforcement Learning for Internet of Vehicles-Reference-Cited by-同舟云学术

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Novel Edge Caching Approach Based on Multi-Agent Deep Reinforcement Learning for Internet of Vehicles

Published:2023-08 Issue:8 Volume:24 Page:8324-8338
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Zhang Degan¹^ORCID,Wang Wenjing¹^ORCID,Zhang Jie²^ORCID,Zhang Ting³^ORCID,Du Jinyu¹^ORCID,Yang Chun¹

Affiliation:

1. Tianjin Key Laboratory of Intelligent Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, China

2. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China

3. School of Sports Economics and Management, Tianjin University of Sport, Tianjin, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/10202228/10098961.pdf?arnumber=10098961

Reference48 articles.

1. Cooperative Content Caching in 5G Networks with Mobile Edge Computing

2. Cooperative vehicular content distribution in edge computing assisted 5G-VANET

3. Optimal Base Station Antenna Downtilt in Downlink Cellular Networks

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

5. A novel approach to mapped correlation of ID for RFID anti-collision;wang;IEEE Trans Services Comput,2014

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