Call admission control for video sources in ATM networks using neural networks

Pao Yue-kong Library Electronic Theses Database

Call admission control for video sources in ATM networks using neural networks

 

Author: Chan, Wing-tsun
Title: Call admission control for video sources in ATM networks using neural networks
Degree: M.Sc.
Year: 1998
Subject: Telecommunication -- Traffic
Neural networks (Computer science)
Asynchronous transfer mode
Image transmission
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Pages: 79 leaves : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1421129
URI: http://theses.lib.polyu.edu.hk/handle/200/5184
Abstract: The role of the call admission control (CAC) in Asynchronous Transfer Mode (ATM) networks becomes more and more important in order to allocate the network resources in a more efficient way yet at the same time, maintain the desired QoS for all the connections. Since there is a large bit-rate variation of digitized video data, the characterization of the video traffic and hence the CAC for such sources are in general difficult in such a dynamic environment. Neural networks are usually employed as an adaptive controller for dynamically changing situations. This project investigates the application of neural networks in CAC in ATM networks. Video sources are modeled by a first order autoregressive process. Training patterns are obtained based on delay requirement for video transmission and are used to train a feedforward neural network using mean, variance and peak values. The performance of the neural CAC is compared with those of other CAC algorithms. The proposed neural CAC is shown to outperform other CAC algorithms in term of average throughput. The adaptive nature of the neural CAC may be particularly suitable for heterogeneous sources.

Files in this item

Files Size Format
b14211294.pdf 2.954Mb PDF
Copyright Undertaking
As a bona fide Library user, I declare that:
  1. I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
  2. I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
  3. I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.

     

Quick Search

Browse

More Information