.png){kind=icon}
返回
Mobile Data Traffic Prediction by Exploiting Time-Evolving User Mobility Patterns
.png){kind=icon}
PRE
AI
DOI：10.1109/TMC.2021.3079117.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Understanding mobile data traffic and forecasting future traffic trend is beneficial to wireless carriers and service providers who need to perform resource allocation and energy saving management. However, predicting wireless traffic accurately at large-scale and fine-granularity is particularly challenging due to the following two factors: the spatial correlations between the network units (i.e., a cell tower or an access point) introduced by user arbitrary movements, and the time-evolving nature of user movements which frequently changes with time. In this paper, we use a time-evolving graph to formulate the time-evolving nature of user movements, and propose a model Graph-based Temporal Convolutional Network (GTCN) to predict the future traffic of each network unit in a wireless network. GTCN can bring significant benefits to two aspects. (1) GTCN can effectively learn intra- and inter-time spatial correlations between network units in a time-evolving graph through a node aggregation method. (2) GTCN can efficiently model the temporal dynamics of the mobile traffic trend from different network units through a temporal convolutional layer. Experimental results on two real-world datasets demonstrate the efficiency and efficacy of our method. Compared with state-of-the-art methods, the improvement of the prediction performance of our GTCN is 3.2 to 10.2 percent for different prediction horizons. GTCN also achieves 8.4x faster on prediction time.
Keyword:
Market research
Wireless fidelity
Predictive models
Load modeling
Adaptation models
Wireless networks
Convolution
User mobility
graph convolution
mobile computing
time series analysis
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.2
论文数:
5.9K
被引数:
1.8W
机构
引用论文
.png){kind=icon}
分享收藏Spectroscopic Study of Aqueous H2SO4 at Different Temperatures and Compositions: Variations in Dissociation and Optical Properties在不同温度和组成下对水溶液H2SO4 的光谱研究: 解离和光学性质的变化
PREAI
分享收藏GARG: Anonymous Recommendation of Point-of-Interest in Mobile Networks by Graph Convolution Network
OAAI
OAAI分享收藏
查看更多内容