Welcome to Journal of Beijing Institute of Technology
Advance Search
Volume 31Issue 5
Oct. 2022
Turn off MathJax
Article Contents
Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2022> 31(5): 441-453
Xiaochuan Sun, Biao Wei, Jiahui Gao, Difei Cao, Zhigang Li, Yingqi Li. Spatio-Temporal Cellular Network Traffic Prediction Using Multi-Task Deep Learning for AI-Enabled 6G[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2022, 31(5): 441-453. doi: 10.15918/j.jbit1004-0579.2022.065
Citation: Xiaochuan Sun, Biao Wei, Jiahui Gao, Difei Cao, Zhigang Li, Yingqi Li. Spatio-Temporal Cellular Network Traffic Prediction Using Multi-Task Deep Learning for AI-Enabled 6G[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2022, 31(5): 441-453.doi:10.15918/j.jbit1004-0579.2022.065
shu

Spatio-Temporal Cellular Network Traffic Prediction Using Multi-Task Deep Learning for AI-Enabled 6G

doi:10.15918/j.jbit1004-0579.2022.065

  • the College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, Hebei, China, and also with the Hebei Key Laboratory of Industrial Perception, Tangshan 063210, Hebei, China

Funds:This work was supported in part by the Science and Technology Project of Hebei Education Department (No. ZD2021088), and in part by the S&T Major Project of the Science and Technology Ministry of China (No. 2017YFE0135700).
More Information
  • Author Bio:

    Xiaochuan Sunreceived the M.S. degree from Guilin University of Electronic Technology, Guilin, China, in 2010, and the Ph.D. degree from Beijing University of Posts and Telecommunications, Beijing, China, 2013. He is currently a Post-Doctoral Fellow with the School of Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China. His current research interests include reservoir computing, neural networks, and deep learning architectures with applications in the domains of time series prediction, classification, and nonlinear system identification

    Biao Weireceived the B.E. degree from Qinggong College, North China University of Science and Technology, Tangshan, China, in 2020. Now he is studying for the postgraduate degree at the College of Artificial Intelligence, North China University of Science and Technology, Tangshan, China. His current research interests include deep learning and spatio-temporal data analysis and prediction

    Jiahui Gaoreceived the B.E. degree from Zaozhuang University, Zaozhuang, China, in 2020. Now she is studying for the postgraduate degree at the college of Artificial Intelligence, North China University of Science and Technology. Her current research interests include machine learning and reservoir computing theory

    Difei Caoreceived the B.E. degree from Hengshui University, Hengshui, China, in 2019. Now he is studying for a post-graduate degree at North China University of Science and Technology, Tangshan, China. His current research interests include neural computing and reservoir computing theory

    Zhigang Lireceived the M.S. degree from Shanxi Agricultural University, Shanxi, China, in 1993, and the Ph.D. degree from China University of Mining and Technology, Beijing, China, 2007. From 2010 to 2012, he has been a Post-Doctoral Research Fellow of the Research Station of Beijing Jiaotong University. He has been a Professor in North China University of Science and Technology, Tangshan, China, since 2003, where he is a dean of the College of Artificial Intelligence. He is currently vice President of the Hebei Computer Society and Hebei Electronics Society. His current research content focuses on the directions of data mining and intelligent control theory

    Yingqi Lireceived the B.S. and M.S. degrees in Guilin University of Electronic Technology, Guilin, China, in 2007 and 2010, respectively. She has been with College of Information Engineering, North China University of Science and Technology, Tangshan, since 2010, and is currently a lecturer. Her current research interests include pattern recognition, evolutionary computation and neural networks

  • Corresponding author:liyingqi@ncst.edu.cn
  • Received Date:2022-05-28
  • Rev Recd Date:2022-07-18
  • Accepted Date:2022-08-16
  • Publish Date:2022-10-31
    Abstract
  • Spatio-temporal cellular network traffic prediction at wide-area level plays an important role in resource reconfiguration, traffic scheduling and intrusion detection, thus potentially supporting connected intelligence of the sixth generation of mobile communications technology (6G). However, the existing studies just focus on the spatio-temporal modeling of traffic data of single network service, such as short message, call, or Internet. It is not conducive to accurate prediction of traffic data, characterised by diverse network service, spatio-temporality and supersize volume. To address this issue, a novel multi-task deep learning framework is developed for citywide cellular network traffic prediction. Functionally, this framework mainly consists of a dual modular feature sharing layer and a multi-task learning layer (DMFS-MT). The former aims at mining long-term spatio-temporal dependencies and local spatio-temporal fluctuation trends in data, respectively, via a new combination of convolutional gated recurrent unit (ConvGRU) and 3-dimensional convolutional neural network (3D-CNN). For the latter, each task is performed for predicting service-specific traffic data based on a fully connected network. On the real-world Telecom Italia dataset, simulation results demonstrate the effectiveness of our proposal through prediction performance measure, spatial pattern comparison and statistical distribution verification.
    • FullText(HTML)
  • loading
    • References (16)
  • [1]
    N. A. Jazea and A. M. Saeed,“Characteristics analysis of (6G) wireless networks: Review, vision, challenges,” American Academic Scientific Research Journal for Engineering, Technology, and Sciences, vol. 87, no. 1, pp. 218-229, 2022.
    [2]
    W. Long, R. Chen, M. Moretti, W. Zhang, and J. Li,“A promising technology for 6G wireless networks: Intelligent reflecting surface,” Journal of Communications and Information Networks, vol. 6, no. 1, pp. 1-16, 2021.
    [3]
    X. An, J. Wu, W. Tong, P. Zhu, and Y. Chen, “6G network architecture vision,” in 2021 Joint European Conference on Networks and Communications& 6G Summit( EuCNC/ 6G Summit), Porto, Portugal, pp. 592-597, 2021.
    [4]
    C. Zhang, H. Zhang, D. Yuan, and M. Zhang,“Citywide cellular traffic prediction based on densely connected convolutional neural networks,” IEEE Communications Letters, vol. 22, no. 8, pp. 1656-1659, 2018. doi:10.1109/LCOMM.2018.2841832
    [5]
    C. Zhang, H. Zhang, J. Qiao, D. Yuan, and M. Zhang,“Deep transfer learning for intelligent cellular traffic prediction based on cross-domain big data,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 6, pp. 1389-1401, 2019. doi:10.1109/JSAC.2019.2904363
    [6]
    K. He, X. Chen, Q. Wu, S. Yu, and Z. Zhou,“Graph attention spatial-temporal network with collaborative global-local learning for citywide mobile traffic prediction,” IEEE Transactions on Mobile Computing, vol. 21, no. 4, pp. 1244-1256, 2022. doi:10.1109/TMC.2020.3020582
    [7]
    Q. Zeng, Q. Sun, G. Chen, and H. Duan,“Attention based multi-component spatiotemporal cross-domain neural network model for wireless cellular network traffic prediction,” EURASIP Journal on Advances in Signal Processing, vol. 2021, no. 1, pp. 1-25, 2021. doi:10.1186/s13634-020-00710-6
    [8]
    N. Zhao, Z. Ye, Y. Pei, Y. C. Liang, and D. Niyato,“Spatial-temporal attention-convolution network for citywide cellular traffic prediction,” IEEE Communications Letters, vol. 24, no. 11, pp. 2532-2536, 2020. doi:10.1109/LCOMM.2020.3012279
    [9]
    K. Zhang, X. Zhao, X. Li, X. You, and Y. Zhu, “Network traffic prediction via deep graph-sequence spatiotemporal modeling based on mobile virtual reality technology,” Wireless Communications and Mobile Computing, vol. 2021, 2021.
    [10]
    C. W. Huang, C. T. Chiang, and Q. Li, “A study of deep learning networks on mobile traffic forecasting,” in 2017 IEEE 28th annual international symposium on personal, indoor, and mobile radio communications( PIMRC), Montreal, QC, Canada, pp. 1-6, 2017.
    [11]
    W. Shen, H. Zhang, S. Guo, and C. Zhang,“Time-wise attention aided convolutional neural network for data-driven cellular traffic prediction,” IEEE Wireless Communications Letters, vol. 10, no. 8, pp. 1747-1751, 2021. doi:10.1109/LWC.2021.3078745
    [12]
    C. Ma, G. Dai, and J. Zhou,“Short-term traffic flow prediction for urban road sections based on time series analysis and lstm−bilstm method,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 6, pp. 5615-5624, 2022. doi:10.1109/TITS.2021.3055258
    [13]
    H. Wu, X. Ma, and Y. Li,“Spatiotemporal multimodal learning with 3d cnns for video action recognition,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 3, pp. 1250-1261, 2022. doi:10.1109/TCSVT.2021.3077512
    [14]
    R. He, Y. Liu, Y. Xiao, X. Lu, and S. Zhang, “Deep spatio-temporal 3d densenet with multiscale convlstm-resnet network for citywide traffic flow forecasting,” Knowledge-Based Systems, vol. 250, pp. 109054-109071, 2022.
    [15]
    T. Yu, Q. Kuang, and R. Yang, “Atmconvgru for weather forecasting,” IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2021.
    [16]
    J. Han, J. Pei, and M. Kamber, Data mining: Concepts and techniques. Amsterdam, Netherlands: Elsevier, 2011, pp.88-89.
    • Relative Articles
  • Supplements (0)
  • Cited By
  • 加载中

Catalog

    通讯作者:陈斌, bchen63@163.com
    • 1.

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)/Tables(3)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (390) PDF downloads(56) Cited by()
    Proportional views
    Related

    Address:5 Zhongguancun South Street, Haidian District, Beijing

    Tel:86-10-68914374

    Email:blgywb@bit.edu.cn

    Copy Right © Journal of Beijing Institute of Technology

    Supported by:Beijing Renhe Information Technology Co. Ltd

    /

    Return
    Return
      Baidu
      map