Communication resource allocation method in vehicular networks based on federated multi-agent deep reinforcement learning
Abstract In highly dynamic vehicular networking scenarios, when Vehicle-to-Infrastructure links and Vehicle-to-Vehicle links share spectrum resources, the traditional distributed resource allocation method lacks global optimization and fails to respond to environmental changes in a timely manner, wh...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-15982-x |
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| author | Qingli Liu Yongjie Ma |
| author_facet | Qingli Liu Yongjie Ma |
| author_sort | Qingli Liu |
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| description | Abstract In highly dynamic vehicular networking scenarios, when Vehicle-to-Infrastructure links and Vehicle-to-Vehicle links share spectrum resources, the traditional distributed resource allocation method lacks global optimization and fails to respond to environmental changes in a timely manner, which leads to low spectral efficiency of the system. A resource allocation method based on federated multi-agent deep reinforcement learning is proposed for Vehicular Networking communication, by fusing Asynchronous Federated Learning (AFL) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG). Synergistic optimization of resource allocation. First, vehicles as agent dynamically optimize spectrum access, power control, and bandwidth allocation based on local channel states through the collaborative policy of MADDPG to reduce cross-link interference. Second, the asynchronous federation architecture is designed, where vehicles independently upload local model parameters to the global server, dynamically adjust the aggregation weights according to the real-time channel quality, and optimize the update of global model parameters. Finally, the global model parameters are fed back to the vehicles to further optimize the local resource allocation strategy, thus improving the system spectrum efficiency. The simulation results show that the system spectrum efficiency is improved by 19.1% on average compared with the centralized DDPG, MADDPG, MAPPO and FL-DuelingDQN algorithms in the Vehicle Networking scenario, while the transmission success rate of the V2V link is improved by 9.3% on average, and the total capacity of the V2I link is increased by 16.1% on average. |
| format | Article |
| id | doaj-art-f5c8a52597934be585b7591c7c0cc201 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-f5c8a52597934be585b7591c7c0cc2012025-08-24T11:25:23ZengNature PortfolioScientific Reports2045-23222025-08-0115111810.1038/s41598-025-15982-xCommunication resource allocation method in vehicular networks based on federated multi-agent deep reinforcement learningQingli Liu0Yongjie Ma1Key Laboratory of Communication and Network, Dalian UniversityKey Laboratory of Communication and Network, Dalian UniversityAbstract In highly dynamic vehicular networking scenarios, when Vehicle-to-Infrastructure links and Vehicle-to-Vehicle links share spectrum resources, the traditional distributed resource allocation method lacks global optimization and fails to respond to environmental changes in a timely manner, which leads to low spectral efficiency of the system. A resource allocation method based on federated multi-agent deep reinforcement learning is proposed for Vehicular Networking communication, by fusing Asynchronous Federated Learning (AFL) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG). Synergistic optimization of resource allocation. First, vehicles as agent dynamically optimize spectrum access, power control, and bandwidth allocation based on local channel states through the collaborative policy of MADDPG to reduce cross-link interference. Second, the asynchronous federation architecture is designed, where vehicles independently upload local model parameters to the global server, dynamically adjust the aggregation weights according to the real-time channel quality, and optimize the update of global model parameters. Finally, the global model parameters are fed back to the vehicles to further optimize the local resource allocation strategy, thus improving the system spectrum efficiency. The simulation results show that the system spectrum efficiency is improved by 19.1% on average compared with the centralized DDPG, MADDPG, MAPPO and FL-DuelingDQN algorithms in the Vehicle Networking scenario, while the transmission success rate of the V2V link is improved by 9.3% on average, and the total capacity of the V2I link is increased by 16.1% on average.https://doi.org/10.1038/s41598-025-15982-xVehicular networksResource allocationDeep reinforcement learningAsynchronous federated learning |
| spellingShingle | Qingli Liu Yongjie Ma Communication resource allocation method in vehicular networks based on federated multi-agent deep reinforcement learning Scientific Reports Vehicular networks Resource allocation Deep reinforcement learning Asynchronous federated learning |
| title | Communication resource allocation method in vehicular networks based on federated multi-agent deep reinforcement learning |
| title_full | Communication resource allocation method in vehicular networks based on federated multi-agent deep reinforcement learning |
| title_fullStr | Communication resource allocation method in vehicular networks based on federated multi-agent deep reinforcement learning |
| title_full_unstemmed | Communication resource allocation method in vehicular networks based on federated multi-agent deep reinforcement learning |
| title_short | Communication resource allocation method in vehicular networks based on federated multi-agent deep reinforcement learning |
| title_sort | communication resource allocation method in vehicular networks based on federated multi agent deep reinforcement learning |
| topic | Vehicular networks Resource allocation Deep reinforcement learning Asynchronous federated learning |
| url | https://doi.org/10.1038/s41598-025-15982-x |
| work_keys_str_mv | AT qingliliu communicationresourceallocationmethodinvehicularnetworksbasedonfederatedmultiagentdeepreinforcementlearning AT yongjiema communicationresourceallocationmethodinvehicularnetworksbasedonfederatedmultiagentdeepreinforcementlearning |