|Title:||Call admission control for video sources in ATM networks using neural networks|
|Subject:||Telecommunication -- Traffic|
Neural networks (Computer science)
Asynchronous transfer mode
Hong Kong Polytechnic University -- Dissertations
|Pages:||79 leaves : ill. ; 30 cm|
|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.|
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