Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning

Published:2023-01 Issue:1 Volume:17 Page:66-81
ISSN:1932-4553
Container-title:IEEE Journal of Selected Topics in Signal Processing
language:
Short-container-title:IEEE J. Sel. Top. Signal Process.

Author:

Wu Qiong¹^ORCID,Zhao Yu¹^ORCID,Fan Qiang²^ORCID,Fan Pingyi³^ORCID,Wang Jiangzhou⁴^ORCID,Zhang Cui⁵

Affiliation:

1. School of Internet of Things Engineering, Jiangnan University, Wuxi, China

2. Qualcomm, San Jose, CA, USA

3. Department of Electronic Engineering and Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China

4. School of Engineering, University of Kent, Canterbury, U.K.

5. Banma Network Technology Co., Ltd., Shanghai, China

Funder

National Natural Science Foundation of China

Open Research Fund of State Key Laboratory of Integrated Services Networks

National Key Research and Development Program of China

Higher Education Discipline Innovation Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/4200690/10050192/09944845.pdf?arnumber=9944845

Reference47 articles.

1. Decentralized Power Allocation for MIMO-NOMA Vehicular Edge Computing Based on Deep Reinforcement Learning

2. Learning the MMSE Channel Estimator

3. Blockchain-empowered Federated Learning Approach for An Intelligent and Reliable D2D Caching Scheme

4. Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications

5. Deep Reinforcement Learning for Cooperative Content Caching in Vehicular Edge Computing and Networks

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