DocumentCode :
2246466
Title :
A reinforcement learning approach for QoS/QoE model identification
Author :
Canale, S. ; Delli Priscoli, F. ; Monaco, S. ; Palagi, L. ; Suraci, V.
Author_Institution :
Department of Computer, Control and Management Engineering “Antonio Ruberti” of the University of Rome “La Sapienza”, Rome, Italy
fYear :
2015
fDate :
28-30 July 2015
Firstpage :
2019
Lastpage :
2023
Abstract :
In the last decade, researchers has focused their studies on the mathematical relation between the Quality of Service (QoS) and the user Quality of Experience (QoE). This paper investigates the problem of modelling the user QoE feedback in the next generation networks. The problem has been formulated and solved using a reinforcement learning technique. The proposed approach is innovative since it does not require an explicit knowledge of the mathematical model describing the network dynamics or the QoS/QoE relationship since it is learnt on-line. Simulation results shows that the proposed solution can adapt dynamically to the user behavior.
Keywords :
Estimation; Internet; Irrigation; Learning (artificial intelligence); Mathematical model; Measurement; Quality of service;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (CCC), 2015 34th Chinese
Conference_Location :
Hangzhou, China
Type :
conf
DOI :
10.1109/ChiCC.2015.7259941
Filename :
7259941
Link To Document :
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