燕山大学学报2025，Vol.49Issue(5)：461-470,10.DOI:10.3969/j.issn.1007-791X.2025.05.009
基于多智能体强化学习的D2D通信资源分配算法研究
Research on D2D communication resource allocation algorithm based on multi-agent reinforcement learning
摘要
Abstract
In order to solve the interference problem of Device-to-Device(D2D)communication in cellular network,considering the mobility of cellular users within the cell,a distributed resource allocation algorithm based on double deep Q-network is proposed by introducing simultaneous wireless information and power transfer technology.This algorithm helps D2D link learn the optimal strategy under the constraints of meeting the minimum quality of service requirements of equipment and incomplete channel state information,so as to alleviate the interference in the system,realize distributed resource allocation and maximize the energy efficiency of D2D link.Firstly,the resource allocation problem of D2D communication is expressed as a Markov decision process.And then,the allocation problem is decomposed into two sub problems:power control and channel allocation.The problem is transformed according to reinforcement learning technology,modeled as a resource allocation problem with multiple agents,and a training algorithm is designed.Experimental results show that the proposed allocation algorithm can effectively converge,significantly improve the energy efficiency of D2D link layer and the throughput of D2D link,and has certain feasibility,effectiveness and superiority.关键词
D2D通信/无线携能通信/功率控制/资源分配/多智能体强化学习Key words
D2D communication/simultaneous wireless information and power transfer/power control/resource allocation/multi-agent reinforcement learning分类
信息技术与安全科学引用本文复制引用
李陶深,漆治军,杜利俊..基于多智能体强化学习的D2D通信资源分配算法研究[J].燕山大学学报,2025,49(5):461-470,10.基金项目
国家自然科学基金资助项目(62062008,61762010) （62062008,61762010）
