Jointly Optimal Caching and Routing Using Multi-Agent Reinforcement Learning-Reference-Cited by-同舟云学术

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Jointly Optimal Caching and Routing Using Multi-Agent Reinforcement Learning

Published:2023-05-28 Issue: Volume: Page:
ISSN:
Container-title:ICC 2023 - IEEE International Conference on Communications
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Author:

Yang Meiyi¹,Gao Deyun¹,Foh Chuan Heng²,Liu Sai¹,Qin Yajuan¹

Affiliation:

1. National Engineering Research Center of Advanced Network Technologies, School of Electronic and Information Engineering, Beijing Jiaotong University,Beijing,China,100044

2. Institute for Communication Systems (ICS), University of Surrey,5GIC & 6GIC,Guildford, Surrey,U.K.

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10278505/10278554/10279669.pdf?arnumber=10279669

Reference14 articles.

1. Optimal multipath congestion control and request forwarding in Information-Centric Networks

2. Cooperative Caching and Fetching in D2D Communications - A Fully Decentralized Multi-Agent Reinforcement Learning Approach

3. Joint Optimization of Cooperative Edge Caching and Radio Resource Allocation in 5G-Enabled Massive IoT Networks

4. Joint Caching and Routing in Congestible Networks of Arbitrary Topology

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