@inproceedings{f6e268f9453f428f8e50b455523e40f8,
title = "UAV-assisted data synchronization for digital-twin-enabled vehicular networks",
abstract = "Digital twin (DT) is becoming a promising solution for vehicular networks to improve the interoperability of distributed autonomous driving systems. Mobile edge computing (MEC) has been introduced to provide low-latency services for DT-enabled vehicular networks at the edge of the network. However, it is hard to obtain the dynamic network topology for moving vehicles by the ground-based MEC system, which may deteriorate the service quality for DT synchronization. In this paper, we propose a novel unmanned aerial vehicle (UAV)-assisted synchronization framework for DT-enabled vehicular networks. With the proposed framework, an intelligent resource allocation algorithm is developed to improve UAV resource utilization and maximize the synchronization completion ratio. By leveraging an advantage actor-critic (A2C) algorithm, the synchronization decisions are obtained with low time complexity. Experiment results demonstrate that the proposed algorithm can reduce the synchronization latency and improve the synchronization completion ratio effectively.",
keywords = "deep reinforcement learning, digital twin, resource allocation, synchronization, Unmanned aerial vehicle",
author = "Guoquan Wu and Jiangtian Nie and Jianhang Tang and Yang Zhang and Jiang Xiao and Zehui Xiong",
year = "2023",
month = sep,
day = "5",
doi = "10.1109/ICCC57788.2023.10233530",
language = "English",
series = "2023 IEEE/CIC International Conference on Communications in China, ICCC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE/CIC International Conference on Communications in China, ICCC 2023",
address = "United States",
note = "2023 IEEE/CIC International Conference on Communications in China, ICCC 2023 ; Conference date: 10-08-2023 Through 12-08-2023",
}