Resource Allocation in V2V Communication via Federated Multi-Agent Reinforcement Learning
Zhang, Shilong Yang, Cheng Yu, Tao Liu, Tong Chen, Jinhua Yu, Keping Guizani, Mohsen
Zhang, Shilong
Yang, Cheng
Yu, Tao
Liu, Tong
Chen, Jinhua
Yu, Keping
Guizani, Mohsen
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
With the increasing number of vehicles, the Internet of Vehicles (IoV) exhibits significant dense deployment characteristics, resulting in severe overlaps of communication areas among vehicles and complex interference. To address these issues, the resource allocation problem under complex interference in densely deployed IoV is investigated in this paper. Specifically, an interference hypergraph model is constructed to simultaneously analyze the complex interference relationships among multiple vehicles. Subsequently, the resource allocation problem is transformed into a vertex coloring problem on the hypergraph. Furthermore, a federated double dueling deep Q-Network algorithm is proposed to achieve conflict-free resource allocation while maximizing network throughput. Simulation experiments demonstrate that the proposed method achieves an average network throughput improvement of more than 17.93% compared to the baseline algorithms, showcasing superior network performance in densely deployed IoV scenarios.
Citation
S. Zhang et al., "Resource Allocation in V2V Communication via Federated Multi-Agent Reinforcement Learning," 2025 IEEE 22nd Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 2025, pp. 1-6, doi: 10.1109/CCNC54725.2025.10975832
Source
Consumer Communications and Networking Conference, CCNC IEEE
Conference
2025 IEEE 22nd Consumer Communications & Networking Conference (CCNC)
Keywords
Analytical models, Interference, Reinforcement learning, Throughput, Resource management, Internet of Vehicles, Federated Multi-Agent Reinforcement Learning, V2V communication, Hypergraph, Resource allocation
Subjects
Source
2025 IEEE 22nd Consumer Communications & Networking Conference (CCNC)
Publisher
IEEE