@inproceedings{28aa8c4769e14fdfb3fb6ed9823ab1aa,
title = "Federated Learning Empowered V2V Resource Allocation in IRS-assisted Vehicular Networks",
abstract = "To solve the issue of the low transmission rate and energy efficiency in vehicle-to-vehicle (V2V) communication, a periodic-federated-adjusted double deep Q-network (PFADDQN) algorithm is proposed for intelligent reflecting surface (IRS)assisted vehicular networks. Considering the constraints in channel and power allocation in V2V communication, a joint benefit, which is defined as the combination of transmission success rate and energy consumption, is maximized. By using ajoint federated learning and double deep Q-network approach, the original NPhard optimization problem is solved. Simulation results demonstrate the superiority of our proposed PFADDQN algorithm over other baselines in IRS-assisted vehicular networks.",
keywords = "DDQN, Federated learning, Intelligent reflecting surface, Joint benefit, Reinforcement learning, Vehicular networks",
author = "Liu Lirui and Song Xiaoqin and Lei Lei and Zhang Lijuan",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 8th International Conference on Signal and Image Processing, ICSIP 2023 ; Conference date: 08-07-2023 Through 10-07-2023",
year = "2023",
doi = "10.1109/ICSIP57908.2023.10270860",
language = "English",
series = "2023 8th International Conference on Signal and Image Processing, ICSIP 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "877--881",
booktitle = "2023 8th International Conference on Signal and Image Processing, ICSIP 2023",
address = "United States",
}
