Deep Reinforcement Learning for Computation Rate Maximization in RIS-Enabled Mobile Edge Computing-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning for Computation Rate Maximization in RIS-Enabled Mobile Edge Computing

Published:2024-07 Issue:7 Volume:73 Page:10862-10866
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

Xu Jianpeng¹^ORCID,Ai Bo²^ORCID,Wu Lina³^ORCID,Zhang Yaoyuan¹^ORCID,Wang Weirong¹^ORCID,Li Huiya¹^ORCID

Affiliation:

1. College of Electronic and Information Engineering, Hebei University, Baoding, China

2. State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, China

3. Institute of Applied Mathematics, Beijing Information Science and Technology University, Beijing, China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

National Key Research and Development Program of China

High-Level Talents Research Start-Up Project of Hebei University

Royal Society Newton Advanced Fellowship

Open Research Fund from the Shenzhen Research Institute of Big Data

Natural Science Foundation of Jiangsu Province Major Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/25/10606142/10504542.pdf?arnumber=10504542

Reference18 articles.

1. Energy-Efficient Secure Computation Offloading in Wireless Powered Mobile Edge Computing Systems

2. Socially Aware Energy-Efficient Mobile Edge Collaboration for Video Distribution

3. Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network

4. Towards Interference Suppression: RIS-Aided High-Speed Railway Networks via Deep Reinforcement Learning

5. RIS-Empowered Ambient Backscatter Communication Systems

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