Energy-Efficient Collaborative Multi-Access Edge Computing Via Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Energy-Efficient Collaborative Multi-Access Edge Computing Via Deep Reinforcement Learning

Author:

Tan Lin¹^ORCID,Kuang Zhufang¹^ORCID,Gao Jie²^ORCID,Zhao Lian³^ORCID

Affiliation:

1. School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China

2. School of Information Technology, Carleton University, Ottawa, ON, Canada

3. Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University (formerly Ryerson University), Toronto, ON, Canada

Funder

National Natural Science Foundation of China

Hunan Provincial Natural Science Foundation of China

Scientific Research Fund of Hunan Provincial Education Department

National Key R&D Program of China

Natural Sciences and Engineering Research Council of Canada

Hunan Key Laboratory of Intelligent Logistics Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

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