Learning-based Call Admission Control Framework for QoS Management in Heterogeneous Network

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

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

Abstract

This paper presents a novel framework for Quality of Service (QoS) management based on the supervised learning approach, Bayesian Belief Networks (BBNs). Apart from proposing the conceptual framework, it provides solution to the problem of Call Admission Control (CAC) in the converged IP-based Next Generation Network (NGN). A detailed description of the modelling procedure and the mathematical underpinning is presented to demonstrate the applicability of our approach. Finally, the theoretical claims have been substantiated through simulations and comparative results are provided as a proof of concept.
Original languageEnglish
Title of host publicationNetworked Digital Technologies
PublisherSpringer
Pages99-111
ISBN (Print)978-3-642-14305-2 (Print) 978-3-642-14306-9 (Online)
DOIs
Publication statusPublished - 30 Jun 2010

Keywords

  • Quality of Service (QoS)
  • Call Admission Control (CAC)
  • Bayesian Belief Networks (BBNs)
  • Next Generation Network (NGN)

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