数据采集与处理2025，Vol.40Issue(5)：1239-1249,11.DOI:10.16337/j.1004-9037.2025.05.010
面向6G无线组网的基于GCN-LSTM网络的业务流量预测算法
Service Traffic Prediction Algorithm Based on GCN-LSTM Network for 6G Wireless Networking
摘要
Abstract
With the rapid development of mobile communication technology,wireless networks are facing multiple challenges,including resource allocation,traffic analysis,and 6G base station optimization.Effective prediction of wireless network traffic helps to allocate network resources reasonably and provides users with more stable and efficient services,ensuring network performance.To solve the problem of low prediction accuracy in the current wireless network traffic predictions due to insufficient mining of spatial and temporal features,this paper conducts research on intelligent traffic prediction algorithms based on deep learning methods,and proposes a prediction algorithm based on graph convolutional network-long short-term memory(GCN-LSTM)model.Experimental results show that the accuracy of this algorithm is 84.71%in actual network applications,which is superior to other deep learning-based traffic prediction methods,providing strong support for the rational allocation of 6G network resources and efficient service.关键词
无线网络流量预测/深度学习/图卷积神经网络/长短期记忆/时空特征挖掘Key words
wireless network traffic prediction/deep learning/graph convolutional network(GCN)/long short-term memory(LSTM)/spatial and temporal features mining分类
信息技术与安全科学引用本文复制引用
孙诗蕾,徐澍,李春国,杨绿溪..面向6G无线组网的基于GCN-LSTM网络的业务流量预测算法[J].数据采集与处理,2025,40(5):1239-1249,11.基金项目
国家重点研发计划(2024YFC3807900,2024YFE0200701) （2024YFC3807900,2024YFE0200701）
国家自然科学基金(62171119) （62171119）
江苏省前沿引领技术基础研究重大项目(BK20222001). （BK20222001）
