Throughput Prediction Using Recurrent Neural Network Model

Bo Wei, Mayuko Okano, Kenji Kanai, Wataru Kawakami, Jiro Katto

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

Abstract

To ensure good quality of experience for user when transmitting video content, throughput prediction can contribute to the selection of proper bitrate. In this paper, we propose a throughput prediction method with recurrent neural network (RNN) model. Experiments are conducted to evaluate the methods, and the results indicate that proposed method can decrease the prediction error by a maximum of 29.39% compared with traditional methods.

Original languageEnglish
Title of host publication2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages88-89
Number of pages2
ISBN (Electronic)9781538663097
DOIs
Publication statusPublished - 2018 Dec 12
Event7th IEEE Global Conference on Consumer Electronics, GCCE 2018 - Nara, Japan
Duration: 2018 Oct 92018 Oct 12

Publication series

Name2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018

Other

Other7th IEEE Global Conference on Consumer Electronics, GCCE 2018
CountryJapan
CityNara
Period18/10/918/10/12

Keywords

  • Mobile network
  • RNN
  • Throughput prediction

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Instrumentation

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  • Cite this

    Wei, B., Okano, M., Kanai, K., Kawakami, W., & Katto, J. (2018). Throughput Prediction Using Recurrent Neural Network Model. In 2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018 (pp. 88-89). [8574877] (2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCCE.2018.8574877