电讯技术2025，Vol.65Issue(5)：693-699,7.DOI:10.20079/j.issn.1001-893x.240129003
基于异构图神经网络的D2D联合功率分配
D2D Joint Power Allocation Based on Heterogeneous Graph Neural Network
摘要
Abstract
Traditional power allocation algorithms are difficult to obtain channel information in real time in real communication due to the high latency caused by complex matrix operations and iterations,and the current important research direction is to find an effective balance between system performance and computational complexity.For the joint power allocation problem between device-to-device(D2D)users and cellular users,a heterogeneous power control graph neural network(HPCGNN)algorithm is proposed,which aims to maximize the weighted sum of all the users' rate.Firstly,by constructing a heterogeneous graph of interference,the information such as channel and noise is embedded into the nodes and edges of the graph.Then the HPCGNN completes the message passing and updating,and uses unsupervised learning to optimize the deep neural network(DNN)parameters,and ultimately obtains the optimal power allocation.Simulation result shows that compared with other deep learning algorithms,the proposed algorithm can effectively improve the system performance,and can reduce the time complexity by 82％～98％compared with Fractional Programming(FP)at a loss of 5％performance.关键词
D2D/功率分配/异构图神经网络Key words
D2D/power allocation/heterogeneous graph neural network分类
电子信息工程引用本文复制引用
陈发堂,徐霄鹏,王文浩,刘泽..基于异构图神经网络的D2D联合功率分配[J].电讯技术,2025,65(5):693-699,7.基金项目
重庆市自然科学基金创新发展联合基金(中国星网)(CSTB2023NSCQ-LZX0114) （中国星网）
