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

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