research-article

Mobile Data Traffic Prediction by Exploiting Time-Evolving User Mobility Patterns

Authors:

Xiaohong GuanAuthors Info & Claims

IEEE Transactions on Mobile Computing, Volume 21, Issue 12

Pages 4456 - 4470

https://doi.org/10.1109/TMC.2021.3079117

Published: 01 December 2022 Publication History

Abstract

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 8.4× faster on prediction time.

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Cited By

Gong JLi TWang HLiu YWang XWang ZDeng CFeng JJin DLi Y(2024)KGDA: A Knowledge Graph Driven Decomposition Approach for Cellular Traffic PredictionACM Transactions on Intelligent Systems and Technology10.1145/369065015:6(1-22)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3690650
Ma YLi TZhou YYu LJin D(2024)Mitigating Energy Consumption in Heterogeneous Mobile Networks Through Data-Driven OptimizationIEEE Transactions on Network and Service Management10.1109/TNSM.2024.341694721:4(4369-4382)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1109/TNSM.2024.3416947
Wu FLyu FRen JYang PQian KGao SZhang Y(2024)Characterizing Internet Card User Portraits for Efficient Churn Prediction Model DesignIEEE Transactions on Mobile Computing10.1109/TMC.2023.324120623:2(1735-1752)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TMC.2023.3241206
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Index Terms

Mobile Data Traffic Prediction by Exploiting Time-Evolving User Mobility Patterns
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Networks
  1. Network services
  2. Network types
    1. Mobile networks
    2. Wireless access networks

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Mobile Computing

IEEE Transactions on Mobile Computing Volume 21, Issue 12

Dec. 2022

366 pages

ISSN:1536-1233

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1536-1233 © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 December 2022

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Cited By

Gong JLi TWang HLiu YWang XWang ZDeng CFeng JJin DLi Y(2024)KGDA: A Knowledge Graph Driven Decomposition Approach for Cellular Traffic PredictionACM Transactions on Intelligent Systems and Technology10.1145/369065015:6(1-22)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3690650
Ma YLi TZhou YYu LJin D(2024)Mitigating Energy Consumption in Heterogeneous Mobile Networks Through Data-Driven OptimizationIEEE Transactions on Network and Service Management10.1109/TNSM.2024.341694721:4(4369-4382)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1109/TNSM.2024.3416947
Wu FLyu FRen JYang PQian KGao SZhang Y(2024)Characterizing Internet Card User Portraits for Efficient Churn Prediction Model DesignIEEE Transactions on Mobile Computing10.1109/TMC.2023.324120623:2(1735-1752)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TMC.2023.3241206
Gong JLiu YLi TChai HWang XFeng JDeng CJin DLi YRenz MNascimento M(2023)Empowering Spatial Knowledge Graph for Mobile Traffic PredictionProceedings of the 31st ACM International Conference on Advances in Geographic Information Systems10.1145/3589132.3625569(1-11)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3589132.3625569
Gu BZhan JGong SLiu WSu ZGuizani M(2023)A Spatial-Temporal Transformer Network for City-Level Cellular Traffic Analysis and PredictionIEEE Transactions on Wireless Communications10.1109/TWC.2023.327044122:12(9412-9423)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TWC.2023.3270441
Wang ZWong V(2023)Bayesian Meta-Learning for Adaptive Traffic Prediction in Wireless NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2023.332530123:6(6620-6633)Online publication date: 17-Oct-2023
https://dl.acm.org/doi/10.1109/TMC.2023.3325301
Shi YLian LShi YWang ZZhou YFu LBai LZhang JZhang W(2023)Machine Learning for Large-Scale Optimization in 6G Wireless NetworksIEEE Communications Surveys & Tutorials10.1109/COMST.2023.330066425:4(2088-2132)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/COMST.2023.3300664
Ma BKuang HLiu SLi C(2023)UAV assisted cellular network traffic offloadingComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2023.109812231:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.comnet.2023.109812

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