Cellular Network Traffic Prediction Incorporating Handover: A Graph Convolutional Approach

Shuai Zhao, Xiaopeng Jiang, Guy Jacobson, Rittwik Jana, Wen Ling Hsu, Raif Rustamov, Manoop Talasila, Syed Anwar Aftab, Yi Chen, Cristian Borcea

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Cellular traffic prediction enables operators to adapt to traffic demand in real-time for improving network resource utilization and user experience. To predict cellular traffic, previous studies either applied Recurrent Neural Networks (RNN) at individual base stations or adapted Convolutional Neural Networks (CNN) to work at grid-cells in a geographically defined grid. These solutions do not consider explicitly the effect of handover on the spatial characteristics of the traffic, which may lead to lower prediction accuracy. Furthermore, RNN solutions are slow to train, and CNN-grid solutions do not work for cells and are difficult to apply to base stations. This paper proposes a new prediction model, STGCN-HO, that uses the transition probability matrix of the handover graph to improve traffic prediction. STGCN-HO builds a stacked residual neural network structure incorporating graph convolutions and gated linear units to capture both spatial and temporal aspects of the traffic. Unlike RNN, STGCN-HO is fast to train and simultaneously predicts traffic demand for all base stations based on the information gathered from the whole graph. Unlike CNN-grid, STGCN-HO can make predictions not only for base stations, but also for cells within base stations. Experiments using data from a large cellular network operator demonstrate that our model outperforms existing solutions in terms of prediction accuracy.

Original languageEnglish (US)
Title of host publication2020 17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728166308
DOIs
StatePublished - Jun 2020
Event17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020 - Virtual, Online, Italy
Duration: Jun 22 2020Jun 25 2020

Publication series

NameAnnual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops
ISSN (Print)2155-5486
ISSN (Electronic)2155-5494

Conference

Conference17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020
Country/TerritoryItaly
CityVirtual, Online
Period6/22/206/25/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Keywords

  • 5G
  • LTE
  • cellular traffic prediction
  • deep learning
  • handover
  • radio access networks
  • spatiooral modeling

Fingerprint

Dive into the research topics of 'Cellular Network Traffic Prediction Incorporating Handover: A Graph Convolutional Approach'. Together they form a unique fingerprint.

Cite this