TRUST: A TCP Throughput Prediction Method in Mobile Networks

Bo Wei, Wataru Kawakami, Kenji Kanai, Jiro Katto, Shangguang Wang

研究成果: Conference contribution

17 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781538647271
DOI
出版ステータスPublished - 2018
イベント2018 IEEE Global Communications Conference, GLOBECOM 2018 - Abu Dhabi, United Arab Emirates
継続期間: 2018 12月 92018 12月 13

出版物シリーズ

名前2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings

Conference

Conference2018 IEEE Global Communications Conference, GLOBECOM 2018
国/地域United Arab Emirates
CityAbu Dhabi
Period18/12/918/12/13

ASJC Scopus subject areas

  • 情報システムおよび情報管理
  • 再生可能エネルギー、持続可能性、環境
  • 安全性、リスク、信頼性、品質管理
  • 信号処理
  • モデリングとシミュレーション
  • 器械工学
  • コンピュータ ネットワークおよび通信

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