HOAH: A hybrid TCP throughput prediction with Autoregressive Model and Hidden Markov Model for mobile networks

Bo Wei, Kenji Kanai, Wataru Kawakami, Jiro Katto

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


Throughput prediction is one of the promising techniques to improve the quality of service (QoS) and quality of experience (QoE) of mobile applications. To address the problem of predicting future throughput distribution accurately during the whole session, which can exhibit large throughput fluctuations in different scenarios (especially scenarios of moving user), we propose a history-based throughput prediction method that utilizes time series analysis and machine learning techniques for mobile network communication. This method is called the Hybrid Prediction with the Autoregressive Model and Hidden Markov Model (HOAH). Different from existing methods, HOAH uses Support Vector Machine (SVM) to classify the throughput transition into two classes, and predicts the transmission control protocol (TCP) throughput by switching between the Autoregressive Model (AR Model) and the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM). We conduct field experiments to evaluate the proposed method in seven different scenarios. The results show that HOAH can predict future throughput effectively and decreases the prediction error by a maximum of 55.95% compared with other methods.

Original languageEnglish
Pages (from-to)1612-1624
Number of pages13
JournalIEICE Transactions on Communications
Issue number7
Publication statusPublished - 2018 Jul


  • Autoregressive model
  • Hidden Markov model
  • Mobile networks
  • Support vector machine
  • Throughput prediction

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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