A study of deep learning networks on mobile traffic forecasting

Chih Wei Huang, Chiu Ti Chiang, Qiuhui Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

52 Scopus citations

Abstract

With evolution toward the fifth generation (5G) cellular technologies, forecasting and understanding of mobile Internet traffic based on big data is the foundation to enable intelligent management features. To take full advantage of machine learning, a more comprehensive investigation on a mobile traffic dataset with the latest deep learning models is desired. Therefore, a multitask learning architecture using deep learning networks for mobile traffic forecasting is presented in this work. State-of-the-art deep learning models are studied, including 1) recurrent neural network (RNN), 2) three-dimensional convolutional neural network (3D CNN), and 3) combination of CNN and RNN (CNN-RNN). The experiments reveal that CNN and RNN can extract geographical and temporal traffic features respectively. Comparing with either deep or non-deep learning approaches, CNN-RNN is a reliable model leading in all tasks with 70 to 80% forecasting accuracy.

Original languageEnglish
Title of host publication2017 IEEE International Symposium on Personal, Indoor and Mobile Radio Communications
Subtitle of host publicationEngaged Citizens and their New Smart Worlds, PIMRC 2017 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538635315
DOIs
StatePublished - 14 Feb 2018
Event28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2017 - Montreal, Canada
Duration: 8 Oct 201713 Oct 2017

Publication series

NameIEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
Volume2017-October

Conference

Conference28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2017
Country/TerritoryCanada
CityMontreal
Period8/10/1713/10/17

Keywords

  • Big data
  • Deep learning
  • Mobile traffic forecasting
  • Multitask learning

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