Computer Science ›› 2022, Vol. 49 ›› Issue (4): 321-328.doi: 10.11896/jsjkx.210300240

• Computer Network • Previous Articles     Next Articles

Traffic Prediction Method for 5G Network Based on Generative Adversarial Network

GAO Zhi-yu, WANG Tian-jing, WANG Yue, SHEN Hang, BAI Guang-wei   

  1. College of Computer Science and Technology, Nanjing Tech University, Nanjing 211816, China
  • Received:2021-03-23 Revised:2021-07-30 Published:2022-04-01
  • About author:GAO Zhi-yu,born in 1996,postgra-duate.His main research interests include network flow prediction and load balancing.
  • Supported by:
    This work was supported by the Natural Science Foundation of Jiangsu Province(BK20201357),University-Industry Collaborative Education Program of the Ministry of Education(201902182003),Six Talent Peak High-level Talent Project of Jiangsu Province(RJFW-020),Jiangsu Key Laboratory Project of Big Data Security and Intelligent Processing(Nanjing University of Posts and Telecommunications)(BDSIP1910),State Key Laboratory Project of New Computer Software Technology(Nanjing University)(KFKT2017B21) and Jiangsu Graduate Scientific Research and Practice Innovation Plan(SJCX21_0486).

Abstract: With the explosive growth of wireless access user demand, 5G network traffic is increasing exponentially and showing a trend of diversity and heterogeneity, which made the network traffic prediction face many challenges.Due to the multi-layer architecture of macro base station, micro base station and pico base station in 5G network, a traffic prediction method based on ge-nerative adversarial network (GAN) is proposed.First, the generation network captures the temporal-spatial features of network traffic and the type features of base station, and then the splicing feature is inputted into the composite residual module to gene-rate the predictive traffic, which is inputted into the discriminant network.Second, the discriminant network determines whether the generative traffic is real traffic or predictive traffic.Finally, after the game confrontation between the generation network and the discriminant network, the generation network could generate high-precision predictive traffic.The experimental results show that, compared with 2DCNN, 3DCNN and ConvLSTM, the two-dimensional root mean square predictive error of GAN is reduced by 58.64%, 38.74% and 34.88%, respectively.Therefore, GAN has the best performance of traffic prediction.

Key words: 5G network, Generative adversarial networks, Spatial-temporal feature mining, Traffic prediction

CLC Number: 

  • TP393
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