Access flow control scheme for ATM networks using neural-network-based traffic prediction

The authors propose a new approach to the problem of congestion control arising at the user network interface (UNI) of ATM-based broadband networks. The access flow control mechanism operates on the principle of feedback control. It uses a finite impulse response (FIR) neural network to accurately predict the traffic arrival patterns. The predicted output in conjunction with the current queue information of the buffer can be used as a measure of congestion. When the congestion level is reached, a control signal is generated to throttle the input arrival rate. The FIR multilayer perceptron model and its training algorithm are discussed. Simulation results presented in the paper suggest that the scheme provides a simple and efficient traffic management for ATM networks.