Backward predictive congestion control notification in ATM networks using neural network prediction

Author

Benjapolakul, Watit ; Niruntasukrat, A.

Author_Institution

Dept. of Electr. Eng., Chulalongkorn Univ., Bangkok

Volume

3

fYear

2001

fDate

6-9 May 2001

Firstpage

165

Abstract

This paper presents neural network predictor used for traffic control in the Backward Predictive Congestion Control Notification (BPCN) scheme in Asynchronous Transfer Mode (ATM) network. The purpose of this study is to compare with the performance of traffic controllability using a Recursive Least Square (RLS) predictor. According to the results, the loss ratio of the traffic is reduced to 18% and the transmission delay is reduced to 88% compared with the cases of the RLS predictors

Keywords

asynchronous transfer mode; backpropagation; controllability; delays; digital communication; least squares approximations; neural nets; nonlinear control systems; predictive control; recursive estimation; telecommunication congestion control; ATM networks; Asynchronous Transfer Mode; RLS predictors; backward predictive congestion control; loss ratio; neural network prediction; recursive least square predictor; simulation model; traffic control; traffic controllability; transmission delay; Asynchronous transfer mode; Bit rate; Buffer storage; Communication system traffic control; Controllability; Intelligent networks; Neural networks; Switches; Telecommunication traffic; Traffic control;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2001. ISCAS 2001. The 2001 IEEE International Symposium on

Conference_Location

Sydney, NSW

Print_ISBN

0-7803-6685-9

Type

conf

DOI

10.1109/ISCAS.2001.921272

Filename

921272