Performance evaluation of multi-service UMTS core networks with clustering and neural modelling
by Izabella Lokshina
International Journal of Mobile Network Design and Innovation (IJMNDI), Vol. 4, No. 1, 2012

Abstract: In this paper, we present the modelling of the dynamic behaviour of an ATM-based, multi-service UMTS core network with calls that belong to one of four service classes and arrive randomly. Arriving calls are granted service based on specific service class, required maximum and minimum bandwidth, and available network resources. Performance of priority-based dynamic capacity allocation, suitable for the wireless system supporting ATM-like traffic is analysed. Scheduling of the ATM cell transmission in each uplink TDMA frame is based on a priority scheme. GoS (blocking probability) and QoS (throughput) parameters for bandwidth sharing policy (BSP) are considered, and partial overlapped transmission link (POL) is implemented. In the modelling, the clustering procedure is developed based on Markov reward models (MRMs), enhanced by the self-organising vector quantification (VQ) and neural modelling. The optimal link occupancy probability distribution is calculated using the neural network, trained with Kohonen rules. Simulation and numerical results are shown.

Online publication date: Thu, 23-Oct-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Mobile Network Design and Innovation (IJMNDI):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com