中南民族大学学报（自然科学版）2024，Vol.43Issue(3)：401-407,7.DOI:10.20056/j.cnki.ZNMDZK.20240315
基于FL-MADQN算法的NR-V2X车载通信频谱资源分配
Spectrum resource allocation for NR-V2X in-vehicle communication based on FL-MADQN algorithm
摘要
Abstract
To address the spectrum resource allocation problem of shared uplink between vehicle-to-infrastructure(V2I)and vehicle-to-vehicle(V2V)in 5G New Radio-Vehicle to Everything(NR-V2X)scenario.A Federated Learning-Multi-Agent Deep Q Network(FL-MADQN)algorithm is proposed.In the decentralized algorithm,each vehicle user is treated as an agent to learn the local network model using the DQN algorithm based on the obtained local channel state information and the optimal network channel capacity as the objective function.Federated learning is used to speed up and stabilize the convergence rate of each agent ′s model training.The local model of each agent is uploaded to the base station for aggregation to form the global model,and then the global model is distributed to each agent to update the local model.Simulation results show that this scheme has a faster model convergence speed compared with the traditional distributed multi-agent DQN algorithm,and the communication efficiency of the V2V link and the channel capacity of the V2I link are still guaranteed when the number of vehicle users increases.关键词
车联网/资源分配/深度Q网络/联邦学习Key words
V2X/resource allocation/deep Q network/federated learning分类
信息技术与安全科学引用本文复制引用
李中捷,邱凡,姜家祥,李江虹,贾玉婷..基于FL-MADQN算法的NR-V2X车载通信频谱资源分配[J].中南民族大学学报（自然科学版）,2024,43(3):401-407,7.基金项目
国家自然科学基金资助项目(61379028,61671483) （61379028,61671483）
中央高校基本科研业务费专项资金资助项目(CZY23027) （CZY23027）
