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
N1 - Funding Information:
ACKNOWLEDGMENT This work is supported by JSPS KAKENHI Grant Number 15H01684.
Publisher Copyright:
© 2018 IEEE.
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 -