By Topic

Integration of ATM call admission control and link capacity control by distributed neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Hiramatsu, A. ; NTT Commun. Switching Lab., Tokyo, Japan

An adaptive call admission control using neural networks was recently proposed for asynchronous transfer mode (ATM) communications networks. The author proposes adaptive link capacity control using neural networks. Neural networks are trained to estimate the call loss rate from link capacity and observed traffic, and link capacity assignment is optimized by a random optimization method according to the estimated call loss rate. The integration of adaptive call admission control and adaptive link capacity control yields an efficient ATM traffic control system suitable for multimedia communication services with unknown traffic characteristics. Computer simulation results using a simple network model are also given to evaluate the accuracy and efficiency of the proposed method

Published in:

Selected Areas in Communications, IEEE Journal on  (Volume:9 ,  Issue: 7 )