TY - GEN
T1 - TRUST
T2 - 2018 IEEE Global Communications Conference, GLOBECOM 2018
AU - Wei, Bo
AU - Kawakami, Wataru
AU - Kanai, Kenji
AU - Katto, Jiro
AU - Wang, Shangguang
N1 - Funding Information:
This work is supported by JSPS KAKENHI Grant Number 15H01684.
Publisher Copyright:
© 2018 IEEE.
PY - 2018
Y1 - 2018
N2 - Throughput prediction is essential for ensuring high quality of service for video streaming transmissions. However, current methods are incapable of accurately predicting throughput in mobile networks, especially for moving user scenarios. Therefore, we propose a TCP throughput prediction method TRUST using machine learning for mobile networks. TRUST has two stages: user movement pattern identification and throughput prediction. In the prediction stage, the long short-term memory (LSTM) model is employed for TCP throughput prediction. TRUST takes all the communication quality factors, sensor data and scenario information into consideration. Field experiments are conducted to evaluate TRUST in various scenarios. The results indicate that TRUST can predict future throughput with higher accuracy than the conventional methods, which decreases the throughput prediction error by maximum 44% under the moving bus scenario.
AB - Throughput prediction is essential for ensuring high quality of service for video streaming transmissions. However, current methods are incapable of accurately predicting throughput in mobile networks, especially for moving user scenarios. Therefore, we propose a TCP throughput prediction method TRUST using machine learning for mobile networks. TRUST has two stages: user movement pattern identification and throughput prediction. In the prediction stage, the long short-term memory (LSTM) model is employed for TCP throughput prediction. TRUST takes all the communication quality factors, sensor data and scenario information into consideration. Field experiments are conducted to evaluate TRUST in various scenarios. The results indicate that TRUST can predict future throughput with higher accuracy than the conventional methods, which decreases the throughput prediction error by maximum 44% under the moving bus scenario.
KW - LSTM
KW - machine learning
KW - mobile networks
KW - throughput prediction
UR - http://www.scopus.com/inward/record.url?scp=85063429845&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063429845&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2018.8647390
DO - 10.1109/GLOCOM.2018.8647390
M3 - Conference contribution
AN - SCOPUS:85063429845
T3 - 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
BT - 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 December 2018 through 13 December 2018
ER -
