广东工业大学学报2025，Vol.42Issue(3)：101-110,10.DOI:10.12052/gdutxb.240111
基于深度强化学习的车联网频谱分配研究
Deep Reinforcement Learning-based Spectrum Allocation in Vehicular Networks
摘要
Abstract
A dynamic spectrum allocation scheme is proposed to address the growing number of vehicles and limited spectrum resources in vehicular networks.It integrates the self-attention mechanism and monotonic value function factorisation into deep multi-agent reinforcement learning to optimize channel selection and power levels for vehicle-to-vehicle(V2V)links.The global objective is to maximize the sum throughput of vehicle-to-infrastructure(V2I)links,while meeting the latency and reliability constraints of V2V links.To handle incomplete real-time channel state information due to dynamic environment,a deep recurrent Q-network is deployed for each V2V link,enabling autonomous decision-making based on local observations.To align the local strategy optimization of each V2V link with the global objective,a global mixing network with monotonicity constraints is designed to guide the algorithm training.Additionally,an information interaction model based on the self-attention mechanism further optimizes collaboration between V2V links.Compared with the baselines,the proposed algorithm increases the sum throughput of V2I links by 1.44～8.24 percentage point and reduces the transmission delay of V2V links by 1.93～15.04 percentage point.These results confirm its effectiveness in optimizing channel selection and power levels for improved communication quality.关键词
车联网/频谱分配/强化学习/自注意力机制Key words
vehicular networks/spectrum allocation/reinforcement learning/self-attention mechanism分类
信息技术与安全科学引用本文复制引用
林志聪,王永华,万频..基于深度强化学习的车联网频谱分配研究[J].广东工业大学学报,2025,42(3):101-110,10.基金项目
国家自然科学基金资助项目(61971147) （61971147）
广东省基础与应用基础研究基金资助项目(2023A1515011888) （2023A1515011888）
广东省研究生教育创新计划项目(2024JGXM_049) （2024JGXM_049）
