DocumentCode :
147842
Title :
Survey on machine learning-based QoE-QoS correlation models
Author :
Aroussi, Sana ; Mellouk, Abdelhamid
Author_Institution :
Comput. Sci. Dept., Dahlab Univ., Algiers, Algeria
fYear :
2014
fDate :
27-29 April 2014
Firstpage :
200
Lastpage :
204
Abstract :
The machine learning provides a theoretical and methodological framework to quantify the relationship between user OoE (Quality of Experience) and network QoS (Quality of Service). This paper presents an overview of QoE-QoS correlation models based on machine learning techniques. According to the learning type, we propose a categorization of correlation models. For each category, we review the main existing works by citing deployed learning methods and model parameters (QoE measurement, QoS parameters and service type). Moreover, the survey will provide researchers with the latest trends and findings in this field.
Keywords :
learning (artificial intelligence); quality of experience; quality of service; telecommunication computing; QoE measurement; QoE-QoS correlation model; QoS parameter; QoS service type; machine learning; quality of experience; quality of service; Correlation; Data models; Packet loss; Predictive models; Quality of service; Streaming media; Correlation model; Machine Learning; Quality of Experience; Quality of Service;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computing, Management and Telecommunications (ComManTel), 2014 International Conference on
Conference_Location :
Da Nang
Print_ISBN :
978-1-4799-2904-7
Type :
conf
DOI :
10.1109/ComManTel.2014.6825604
Filename :
6825604
Link To Document :
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