Traffic Forecasting on Mobile Networks Using 3D Convolutional Layers
| Autor: | Oliverio Cruz-Mejía, Alberto Ochoa-Zezzati, Jose Mejia |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer Networks and Communications
business.industry Computer science Deep learning media_common.quotation_subject Real-time computing Cellular traffic 020206 networking & telecommunications 02 engineering and technology Base station Hardware and Architecture 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Network performance The Internet Quality (business) Artificial intelligence business Computer communication networks Software Information Systems media_common |
| Zdroj: | Mobile Networks and Applications. 25:2134-2140 |
| ISSN: | 1572-8153 1383-469X |
| DOI: | 10.1007/s11036-020-01554-y |
| Popis: | Increasing demands to access the internet through mobile infrastructures have in turn increased demands for improved quality and speed in communication services. One possible solution to meet these demands is to use cellular traffic forecasting to improve network performance. In this paper, a model for predicting traffic at a selected cellular base station (BS) is proposed. In the model, spatiotemporal features from neighboring stations to the target BS are used, and this information is used for forecasting through a series of surfaces evolving over time and a deep learning architecture consisting of 3D convolutional networks. Experimental results showed that this method outperformed other approaches used to predict traffic data. |
| Databáze: | OpenAIRE |
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