Adaptive Graph Convolutional Recurrent Neural Networks for System-Level Mobile Traffic Forecasting

作     者：Yi Zhang Min Zhang Yihan Gui Yu Wang Hong Zhu Wenbin Chen Danshi Wang Yi Zhang;Min Zhang;Yihan Gui;Yu Wang;Hong Zhu;Wenbin Chen;Danshi Wang

作者机构：Beijing University of Posts and TelecommunicationsBeijing 100876China The Intelligent Network Innovation Center of ChinaunicomBeijing 100048China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2023年第20卷第10期

页      面：200-211页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 12[管理学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0810[工学-信息与通信工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(61975020 62171053) 

主　　题：adaptive graph convolutional network mobile traffic prediction spatial-temporal dependence 

摘      要：Accurate traffic pattern prediction in largescale networks is of great importance for intelligent system management and automatic resource ***-level mobile traffic forecasting has significant challenges due to the tremendous temporal and spatial dynamics introduced by diverse Internet user behaviors and frequent traffic *** graph modeling is an efficient approach for analyzing the spatial relations and temporal trends of mobile traffic in a large *** research may not reflect the optimal dependency by ignoring inter-base station dependency or pre-determining the explicit geological distance as the interrelationship of base *** overcome the limitations of graph structure,this study proposes an adaptive graph convolutional network(AGCN)that captures the latent spatial dependency by developing self-adaptive dependency matrices and acquires temporal dependency using recurrent neural *** on two mobile network datasets,the experimental results demonstrate that this method outperforms other baselines and reduces the mean absolute error by 3.7%and 5.6%compared to time-series based approaches.