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Intelligent User Profile Prediction in Radio Access Network

  • Yaxing QiuEmail author
  • Xidong WangEmail author
  • Fengjun WangEmail author
  • Sen BianEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 550)

Abstract

With increasingly dense deployment of base stations, power consumption, resource utilization, interference management, and user experience optimization in mobile network have become more and more important challenges. The effective prediction of temporal and spatial data traffic distribution, en-powered by intelligent user profile prediction, will be essential. This paper investigates user mobility and user service patterns prediction by generating refined user trajectory and 2-D service feature models. Specifically, an improved Bayesian trajectory prediction strategy coupled with time-domain features extraction used for user service pattern prediction is proposed. Large scale field test using 2300 active mobile devices (users) across 1600 cells in a live network showed promising results of 85% trajectory prediction accuracy and 70% service pattern prediction accuracy. The accurate prediction of user-level behavior pattern is of great significance not only for the improvement of network energy-efficiency, but also for the guarantee of user experience and the optimization of network utilization.

Keywords

Mobility prediction Service pattern prediction Machine learning 

References

  1. 1.
    China Mobile Communications Corporation: Sustainability Report: Big Connectivity, New Future (2016)Google Scholar
  2. 2.
    Xu, L., Zhao, X., Luan, Y., et al.: User perception aware telecom data mining and network management for LTE/LTE-advanced networks. In: 4th International Conference on Signal and Information Processing, Networking and Computers, pp. 237–245. Springer, Qingdao (2018)Google Scholar
  3. 3.
    Xu, L., Luan, Y., Cheng, X., et al.: Telecom Big Data based user offloading self-optimisation in heterogeneous relay cellular systems. Int. J. Distrib. Syst. Technol. 8(2), 27–46 (2017)CrossRefGoogle Scholar
  4. 4.
    Song, L., Kotz, D., Jain, R., et al.: Evaluating next-cell predictors with extensive Wi-Fi mobility data. IEEE Trans. Mob. Comput. 5(12), 1633–1649 (2006)CrossRefGoogle Scholar
  5. 5.
    Tomar, R.S., Verma, S.: Trajectory prediction of lane changing vehicles using SVM. Int. J. Veh. Saf. 5(4), 345–355 (2011)CrossRefGoogle Scholar
  6. 6.
    Wang, J., Tang, J., Xu, Z., et al.: Spatiotemporal modeling and prediction in cellular networks: a Big Data enabled deep learning approach. In: INFOCOM IEEE Conference on Computer Communications, pp. 1–9. IEEE (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Green Communication Research Center of China Mobile Research InstituteCMCCBeijingPeople’s Republic of China

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