Novel distributed call admission control solution based on machine learning approach

Abul Bashar, Gerard Parr, Sally McClean, Scotney Bryan, Detlef Nauck

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


The advent of IP-based Next Generation Network (NGN) and its guaranteed QoS promise has attracted significant attention from both service providers and subscribers. However, to fulfil the said promise, there is a need to provide effective Call Admission Control (CAC) based QoS provisioning solutions which are autonomous, intelligent and scalable.
Original languageEnglish
Title of host publicationUnknown Host Publication
Number of pages11
ISBN (Print)978-1-4244-9219-0 (print)
Publication statusPublished (in print/issue) - 18 Aug 2011
EventIFIP/IEEE International Symposium on Integrated Network Management - Dublin, Ireland
Duration: 18 Aug 2011 → …


OtherIFIP/IEEE International Symposium on Integrated Network Management
Period18/08/11 → …

Bibliographical note

Reference text: General overview of NGN, ITU-T Recommendation Y, 2001, Dec, 2004,

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A. Bashar, G. P. Parr, S. I. McClean, B. W. Scotney, D. Nauck, "Learning-based call admission control framework for QoS management in heterogeneous networks," In Proc. of Springer LNCS CCIS series, 2nd International Conference on Networked Digital Technologies (NDT 2010), vol. II, pp. 99-111, Jul. 2010.

A. Bashar, G. P. Parr, S. I. McClean, B. W. Scotney, D. Nauck, "Machine Learning based call admission control approaches: A comparative study," in Proc. of IEEE/IFIP 6th International Conference on Network and Service Management (CNSM 2010), Oct. 2010.

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Opnet Modeler 16.0, http://www.opneLcom

Hugin Researcher 7.3,


  • Bayesian methods
  • Call admission control
  • Delay
  • Machine learning
  • Next generation networking
  • Predictive models
  • Support vector machines


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