Cybertwin-Driven Multi-Intelligent Reflecting Surfaces aided Vehicular Edge Computing Leveraged by Deep Reinforcement Learning-Reference-Cited by-同舟云学术

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Cybertwin-Driven Multi-Intelligent Reflecting Surfaces aided Vehicular Edge Computing Leveraged by Deep Reinforcement Learning

Published:2022-09 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)
language:
Short-container-title:

Author:

Zhang Xuhui¹^ORCID,Xing Huijun²,Zang Weilin¹,Jin Zhenzhen³,Shen Yanyan¹

Affiliation:

1. Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen,China

2. The Future Network of Intelligence Institute (FNii), The Chinese University of Hong Kong,Shenzhen,China

3. Southern University of Science and Technology,Shenzhen,China

Funder

Natural Science Foundation of Guangdong Province

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10012685/10012692/10012694.pdf?arnumber=10012694

Reference21 articles.

1. Computation Rate Maximization for IRS-Aided Wireless Powered MEC Systems

2. IRS/UAV-Based Edge-Computing/Traffic-Offloading Over RF-Powered 6G Mobile Wireless Networks

3. Scheduling the Operation of a Connected Vehicular Network Using Deep Reinforcement Learning

4. DRL based Data Offloading for Intelligent Reflecting Surface Aided Mobile Edge Computing

5. Joint 3-D Trajectory and Resource Optimization in Multi-UAV-Enabled IoT Networks With Wireless Power Transfer

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1. Navigating the Road to Connectivity: Use Cases and Design Considerations for V2X Networks Using Millimeter-Wave 5G Beam-Steering Antennas;2023 IEEE Microwaves, Antennas, and Propagation Conference (MAPCON);2023-12-11

2. AoI-Aware UAV-Enabled Marine MEC Networks with Integrated Sensing, Computation, and Communication;2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops);2023-08-10

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