IMPROVEMENT OF MULTIMEDIA SERVICES QUALITY IN NEXT GENERATION NETWORKS USING FUZZY LOGIC METHODS
DOI:
https://doi.org/10.15673/0453-8307.1/2015.31480Keywords:
Quality of services – Next generation networks – Fuzzy logicAbstract
The paper presents a model for determining the degree of user satisfaction with quality of services, based on fuzzy inference on the basis of linguistic rules which are formulated based on user ratings. Proposed model is based on the fuzzy logic methods, because these methods allow evaluating user opinion most effectively. The model is a hierarchical structure that contains the main components of the service quality. For each of the components corresponding quality criteria are represented. On the basis of the proposed model user perception for music streaming and download service was estimated. Simulation was performed in MATLAB using the instruments of Fuzzy Logic Toolbox. The simulation enabled to answer the question which components of the quality of service and which quality criteria cause the maximum effect on the degree of user satisfaction with the service. Moreover, proposed model enables to get in advance an idea of the variation of the degree of satisfaction of a user of a service depending on changes in the level achieved by a particular quality criterion. The number of quality criteria that affect the final index can be easily changed if necessary. The proposed approach can be used to extrapolate and predict the degree of user satisfaction with quality of services on a limited statistical material.
References
Radhakrishnan, K., Larijani, H. 2011. Evaluating perceived voice quality on packet networks using differentrandom neural network architectures. Performance Evaluation, Vol. 68, Issue 4, 347–360. doi:10.1016/j.peva.2011.01.001
Jaber, M., Combaz J., Strus, L., Fernandez, J.C. 2008. Using neural networks for quality management Emerging Technologies and Factory Automation, 2008. ETFA 2008. IEEE International Conference. P. 1441-1448. doi: 10.1109/ETFA.2008.4638586
Golmohammadi, A., Jahandideh, B. 2010. Prioritizing Service Quality Dimensions: A Neural Network Approach. World Academy of Science, Engineering & Technology, Issue 42, 602-605.doi: 10.1108/09604520710817325
Dogman, A., Saatchi, R., Al-Khayatt, S. 2012.Quality of Service Evaluation using a Combination of Fuzzy C-Means and Regression Model. International journal of electronics and electrical engineering, Vol.6, No.1, 58-65.
Dogman, A., Saatchi, R., Al-Khayatt, S. 2012. Evaluation of computer network Quality of Service using neural networks. Business, Engineering and Industrial Applications (ISBEIA), 2012 IEEE Symposium. – P. 217-222. doi: 10.1109/isbeia.2012.6422873
“Mezhdunarodnyy soyuz elektrosvyazi (ITU)”, ofitsial'noye Internet-predstavitel'stvo. – Access mode: http://www.itu.int (access data: 27 Oktober 2014).
Knyazeva, N.A., Kal'chenko, A.S. 2013. Science and Education a New Dimension: Natural and Technical Science. Budapest,– P. 156-161.
Leonenkov, A.V. 2005. – SPb: BHV - Peterburg. – 736 p.
Shtovba, S.D. 2003. Vvedeniye v teoriyu nechetkikh mnozhestv i nechetkuyu logiku. - Vinnitsa: Kontinent-Prim. – 198 p.
