TY - GEN
T1 - Resource Allocation in Vehicular Networks Based on Federated Multi-Agent Reinforcement Learning
AU - Yu, Jiaming
AU - Wu, Shaochuan
AU - Liang, Le
AU - Jin, Shi
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, we propose a distributed resource allocation scheme based on federated multi-agent deep reinforcement learning (Fed-MARL) to address the channel allocation and power control problem in vehicular networks. We tackle the formulated resource optimization problem by taking advantage of deep reinforcement learning and federated learning, to satisfy the different quality-of-service requirements for vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) links. Specifically, we propose to enhance traditional reinforcement learning methods, including both the deep Q network and proximal policy optimization, with federated learning, to obtain two efficient Fed-MARL-based resource allocation algorithms for vehicular networks. Simulation results show that our proposed resource allocation schemes exhibit superiority in both the total capacity of V2I links and the payload delivery rate of V2V links simultaneously, compared to other baselines without federated learning assistance.
AB - In this paper, we propose a distributed resource allocation scheme based on federated multi-agent deep reinforcement learning (Fed-MARL) to address the channel allocation and power control problem in vehicular networks. We tackle the formulated resource optimization problem by taking advantage of deep reinforcement learning and federated learning, to satisfy the different quality-of-service requirements for vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) links. Specifically, we propose to enhance traditional reinforcement learning methods, including both the deep Q network and proximal policy optimization, with federated learning, to obtain two efficient Fed-MARL-based resource allocation algorithms for vehicular networks. Simulation results show that our proposed resource allocation schemes exhibit superiority in both the total capacity of V2I links and the payload delivery rate of V2V links simultaneously, compared to other baselines without federated learning assistance.
KW - Federated learning
KW - reinforcement learning
KW - vehicular networks
KW - wireless resource allocation
UR - https://www.scopus.com/pages/publications/85186070863
U2 - 10.1109/ICCT59356.2023.10419509
DO - 10.1109/ICCT59356.2023.10419509
M3 - 会议稿件
AN - SCOPUS:85186070863
T3 - International Conference on Communication Technology Proceedings, ICCT
SP - 84
EP - 89
BT - 2023 IEEE 23rd International Conference on Communication Technology
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd IEEE International Conference on Communication Technology, ICCT 2023
Y2 - 20 October 2023 through 22 October 2023
ER -