DRL-Based Resource Allocation Game With Influence of Review Information for Vehicular Edge Computing Systems-Reference-Cited by-同舟云学术

DRL-Based Resource Allocation Game With Influence of Review Information for Vehicular Edge Computing Systems

Published:2024-07 Issue:7 Volume:73 Page:9591-9603
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

Zhang Han¹^ORCID,Liang Hongbin¹^ORCID,Hong Xintao²,Yao Yiting¹,Lin Bin³^ORCID,Zhao Dongmei⁴^ORCID

Affiliation:

1. School of Transportation and Logistics, National United Engineering Laboratory of Integrated and Intelligent Transportation, and the National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu, China

2. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China

3. Department of Communication Engineering, Dalian Maritime University, Dalian, China

4. Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/25/10606142/10440466.pdf?arnumber=10440466

Reference43 articles.

1. Computation Migration and Resource Allocation in Heterogeneous Vehicular Networks: A Deep Reinforcement Learning Approach

2. SDN/NFV-Empowered Future IoV With Enhanced Communication, Computing, and Caching

3. Computation Offloading and Resource Allocation For Cloud Assisted Mobile Edge Computing in Vehicular Networks

4. Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks

5. Deep Reinforcement Learning for Offloading and Resource Allocation in Vehicle Edge Computing and Networks