@inproceedings{298ee055af014a48af0ff59480582207,
title = "Machine learning based Call Admission Control approaches: A comparative study",
abstract = "The importance of providing guaranteed Quality of Service (QoS) cannot be overemphasised, especially in the NGN environment which supports converged services on a common IP transport network. Call Admission Control (CAC) mechanisms do provide QoS to class-based services in a proactive manner. However, due to the factors of complexity, scale and dynamicity of NGN, Machine Learning techniques are favoured to analytical approaches for providing autonomous CAC. This paper is an effort to compare the performance of two such approaches - Neural Networks (NN) and Bayesian Networks (BN), to model the network behaviour and to estimate QoS metrics to be used in the CAC algorithm. It provides a way to find the optimum model training size for accurate predictions. Performance comparison is based on a wide range of experiments through a simulated network in Opnet. The outcome of this comparative study provides some interesting insights into the behaviour of NN and BN models and how they can be utilised for better CAC implementations.",
keywords = "Bayesian Networks, Call Admission Control, Machine Learning, Neural Networks, Quality of Service",
author = "Abul Bashar and Gerard Parr and Sally McClean and Scotney Bryan and Detlef Nauck",
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year = "2011",
month = jan,
day = "17",
doi = "10.1109/CNSM.2010.5691261",
language = "English",
isbn = "978-1-4244-8910-7",
pages = "431--434",
booktitle = "Unknown Host Publication",
publisher = "IEEE",
address = "United States",
}