IEICE TRANSACTIONS on Communications
HOAH: A Hybrid TCP Throughput Prediction with Autoregressive Model and Hidden Markov Model for Mobile Networks
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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E101-B No.7 pp.1612-1624
- Publication Date
- 2018/07/01
- Publicized
- 2018/01/22
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2017CQP0007
- Type of Manuscript
- Special Section PAPER (Special Section on Communication Quality in Wireless Networks)
- Category
Authors
Bo WEI
Waseda University
Kenji KANAI
Waseda University
Wataru KAWAKAMI
Waseda University
Jiro KATTO
Waseda University
Keyword
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Bo WEI, Kenji KANAI, Wataru KAWAKAMI, Jiro KATTO, "HOAH: A Hybrid TCP Throughput Prediction with Autoregressive Model and Hidden Markov Model for Mobile Networks" in IEICE TRANSACTIONS on Communications,
vol. E101-B, no. 7, pp. 1612-1624, July 2018, doi: 10.1587/transcom.2017CQP0007.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2017CQP0007/_p
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@ARTICLE{e101-b_7_1612,
author={Bo WEI, Kenji KANAI, Wataru KAWAKAMI, Jiro KATTO, },
journal={IEICE TRANSACTIONS on Communications},
title={HOAH: A Hybrid TCP Throughput Prediction with Autoregressive Model and Hidden Markov Model for Mobile Networks},
year={2018},
volume={E101-B},
number={7},
pages={1612-1624},
abstract={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.},
keywords={},
doi={10.1587/transcom.2017CQP0007},
ISSN={1745-1345},
month={July},}
Copy
TY - JOUR
TI - HOAH: A Hybrid TCP Throughput Prediction with Autoregressive Model and Hidden Markov Model for Mobile Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 1612
EP - 1624
AU - Bo WEI
AU - Kenji KANAI
AU - Wataru KAWAKAMI
AU - Jiro KATTO
PY - 2018
DO - 10.1587/transcom.2017CQP0007
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E101-B
IS - 7
JA - IEICE TRANSACTIONS on Communications
Y1 - July 2018
AB - 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.
ER -
English
