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Multi-Service Mobile Traffic Forecasting via Convolutional Long Short-Term Memories

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https://ieeexplore.ieee.org/document/8804984
Original languageEnglish
Title of host publication2019 IEEE International Workshop on Measurement and Networking (M&N)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-6
Number of pages6
ISBN (Electronic)978-1-7281-1273-2
ISBN (Print)978-1-7281-1274-9
DOIs
Publication statusPublished - 19 Aug 2019
Event5th IEEE International Symposium on Measurements and Networking - Catania, Italy
Duration: 8 Jul 201910 Jul 2019
https://mn2019.ieee-ims.org/

Publication series

Name
PublisherInstitute of Electrical and Electronics Engineers
ISSN (Print)2639-507X
ISSN (Electronic)2639-5061

Conference

Conference5th IEEE International Symposium on Measurements and Networking
Abbreviated titleMN 2019
CountryItaly
CityCatania
Period8/07/1910/07/19
Internet address

Abstract

Network slicing is increasingly used to partition network infrastructure between different mobile services. Precise service-wise mobile traffic forecasting becomes essential in this context, as mobile operators seek to pre-allocate resources to each slice in advance, to meet the distinct requirements of individual services. This paper attacks the problem of multi-service mobile traffic forecasting using a sequence-to-sequence (S2S) learning paradigm and convolutional long short-term memories (ConvLSTMs). The proposed architecture is designed so as to effectively extract complex spatiotemporal features of mobile network traffic and predict with high accuracy the future demands for individual services at city scale. We conduct experiments on a mobile traffic dataset collected in a large European metropolis, demonstrating that the proposed S2S-ConvLSTM can forecast the mobile traffic volume produced by tens of different services in advance of up to one hour, by just using measurements taken during the past hour. In particular, our solution achieves mean absolute errors (MAE) at antenna level that are below 13KBps, outperforming other deep learning approaches by up to 31.2%.

    Research areas

  • Mobile traffic forecasting, deep learning, convolutional long short-tem memory

Event

5th IEEE International Symposium on Measurements and Networking

8/07/1910/07/19

Catania, Italy

Event: Conference

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