HOAH: A Hybrid TCP Throughput Prediction with Autoregressive Model and Hidden Markov Model for Mobile Networks

Bo WEI  Kenji KANAI  Wataru KAWAKAMI  Jiro KATTO  

Publication
IEICE TRANSACTIONS on Communications   Vol.E101-B   No.7   pp.1612-1624
Publication Date: 2018/07/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2017CQP0007
Type of Manuscript: Special Section PAPER (Special Section on Communication Quality in Wireless Networks)
Category: 
Keyword: 
throughput prediction,  Autoregressive Model,  Hidden Markov Model,  mobile networks,  support vector machine,  

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Summary: 
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.