Cellular Network Traffic Prediction Incorporating Handover: A Graph Convolutional Approach

Document Type

Conference Proceeding

Publication Date

6-1-2020

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.

Identifier

85091959684 (Scopus)

ISBN

[9781728166308]

Publication Title

Annual IEEE Communications Society Conference on Sensor Mesh and Ad Hoc Communications and Networks Workshops

External Full Text Location

https://doi.org/10.1109/SECON48991.2020.9158437

e-ISSN

21555494

ISSN

21555486

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