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QoE Management in Wireless NetworksArchitecture of Data-Driven Personalized QoE Management

QoE Management in Wireless Networks: Architecture of Data-Driven Personalized QoE Management [In this chapter, we propose a systematic architecture on data-driven personalized QoE management. A framework of the QoE management architecture is firstly introduced, which consists of two modules namely (1) training module and (2) control module. We also depict two models for the prediction of user preference, including Bayesian Graphic Model and Context Aware Matrix Factorization Model. A preliminary use case is deployed to demonstrate and evaluate the proposed architecture. Simulation results illustrate the superior performance of proposed architecture compared with traditional water-filling method.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

QoE Management in Wireless NetworksArchitecture of Data-Driven Personalized QoE Management

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Publisher
Springer International Publishing
Copyright
© The Author(s) 2017
ISBN
978-3-319-42452-1
Pages
21 –32
DOI
10.1007/978-3-319-42454-5_3
Publisher site
See Chapter on Publisher Site

Abstract

[In this chapter, we propose a systematic architecture on data-driven personalized QoE management. A framework of the QoE management architecture is firstly introduced, which consists of two modules namely (1) training module and (2) control module. We also depict two models for the prediction of user preference, including Bayesian Graphic Model and Context Aware Matrix Factorization Model. A preliminary use case is deployed to demonstrate and evaluate the proposed architecture. Simulation results illustrate the superior performance of proposed architecture compared with traditional water-filling method.]

Published: Aug 2, 2016

Keywords: Control Module; Contextual Factor; Mobile Agent; Information Gain; User Preference

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