Throughput Prediction Using Recurrent Neural Network Model

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

doi:10.1109/GCCE.2018.8574877

Throughput Prediction Using Recurrent Neural Network Model

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Citations (Scopus)

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 language	English
Title of host publication	2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	88-89
Number of pages	2
ISBN (Electronic)	9781538663097
DOIs	https://doi.org/10.1109/GCCE.2018.8574877
Publication status	Published - 2018 Dec 12
Event	7th IEEE Global Conference on Consumer Electronics, GCCE 2018 - Nara, Japan Duration: 2018 Oct 9 → 2018 Oct 12

Publication series

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

Other

Other	7th IEEE Global Conference on Consumer Electronics, GCCE 2018
Country/Territory	Japan
City	Nara
Period	18/10/9 → 18/10/12

Keywords

Mobile network
RNN
Throughput prediction

ASJC Scopus subject areas

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

Access to Document

10.1109/GCCE.2018.8574877

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). Article 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

Throughput Prediction Using Recurrent Neural Network Model. / Wei, Bo; Okano, Mayuko; Kanai, Kenji et al.
2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 88-89 8574877 (2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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., 8574877, 2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018, Institute of Electrical and Electronics Engineers Inc., pp. 88-89, 7th IEEE Global Conference on Consumer Electronics, GCCE 2018, Nara, Japan, 18/10/9. https://doi.org/10.1109/GCCE.2018.8574877

@inproceedings{39d1028e634d47ffb6d91a6ee2df291a,

title = "Throughput Prediction Using Recurrent Neural Network Model",

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.",

keywords = "Mobile network, RNN, Throughput prediction",

author = "Bo Wei and Mayuko Okano and Kenji Kanai and Wataru Kawakami and Jiro Katto",

note = "Funding Information: ACKNOWLEDGMENT This work is supported by JSPS KAKENHI Grant Number 15H01684. Publisher Copyright: {\textcopyright} 2018 IEEE.; 7th IEEE Global Conference on Consumer Electronics, GCCE 2018 ; Conference date: 09-10-2018 Through 12-10-2018",

year = "2018",

month = dec,

day = "12",

doi = "10.1109/GCCE.2018.8574877",

language = "English",

series = "2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "88--89",

booktitle = "2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018",

}

TY - GEN

T1 - Throughput Prediction Using Recurrent Neural Network Model

AU - Wei, Bo

AU - Okano, Mayuko

AU - Kanai, Kenji

AU - Kawakami, Wataru

AU - Katto, Jiro

PY - 2018/12/12

Y1 - 2018/12/12

N2 - 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.

AB - 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.

KW - Mobile network

KW - RNN

KW - Throughput prediction

UR - http://www.scopus.com/inward/record.url?scp=85060293820&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85060293820&partnerID=8YFLogxK

U2 - 10.1109/GCCE.2018.8574877

DO - 10.1109/GCCE.2018.8574877

M3 - Conference contribution

AN - SCOPUS:85060293820

T3 - 2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018

SP - 88

EP - 89

BT - 2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 7th IEEE Global Conference on Consumer Electronics, GCCE 2018

Y2 - 9 October 2018 through 12 October 2018

ER -

Throughput Prediction Using Recurrent Neural Network Model

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this