Augmented Deep Reinforcement Learning for Online Energy Minimization of Wireless Powered Mobile Edge Computing-Reference-Cited by-同舟云学术

Augmented Deep Reinforcement Learning for Online Energy Minimization of Wireless Powered Mobile Edge Computing

Published:2023-05 Issue:5 Volume:71 Page:2698-2710
ISSN:0090-6778
Container-title:IEEE Transactions on Communications
language:
Short-container-title:IEEE Trans. Commun.

Author:

Chen Xiaojing¹^ORCID,Dai Weiheng¹,Ni Wei²^ORCID,Wang Xin³^ORCID,Zhang Shunqing¹^ORCID,Xu Shugong¹^ORCID,Sun Yanzan¹^ORCID

Affiliation:

1. Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai, China

2. Commonwealth Scientific and Industrial Research Organization (CSIRO), Sydney, NSW, Australia

3. Department of Communication Science and Engineering, Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Innovation Program of Shanghai Municipal Science and Technology Commission

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/26/10128759/10057423.pdf?arnumber=10057423

Reference36 articles.

1. Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

2. Power Allocation in Multi-User Cellular Networks: Deep Reinforcement Learning Approaches

3. Optimization of Task Offloading Strategy for Mobile Edge Computing Based on Multi-Agent Deep Reinforcement Learning

4. Deep Reinforcement Learning: A Brief Survey

5. Energy-Delay Tradeoff for Dynamic Offloading in Mobile-Edge Computing System With Energy Harvesting Devices

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