Federated-Reinforcement-Learning-Enabled Joint Communication, Sensing, and Computing Resources Allocation in Connected Automated Vehicles Networks-Reference-Cited by-同舟云学术

Federated-Reinforcement-Learning-Enabled Joint Communication, Sensing, and Computing Resources Allocation in Connected Automated Vehicles Networks

Published:2022-11-15 Issue:22 Volume:9 Page:23224-23240
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Zhang Qixun¹^ORCID,Wen Hao¹,Liu Ying¹,Chang Shuo¹^ORCID,Han Zhu²^ORCID

Affiliation:

1. Key Laboratory of Universal Wireless Communications, Ministry of Education, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China

2. Department of Electrical and Computer Engineering, The University of Houston, Houston, TX, USA

Funder

National Natural Science Foundation of China

National Key Research and Development Project

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/9939125/09815075.pdf?arnumber=9815075

Reference58 articles.

1. When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multitimescale Resource Management for Multiaccess Edge Computing in 5G Ultradense Network

2. Federated Learning over Wireless Networks: Optimization Model Design and Analysis

3. Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

4. Deep Reinforcement Learning for Resource Allocation in V2V Communications

5. Deep Reinforcement Learning Based Resource Allocation for V2V Communications

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