Adaptive traffic rate control of ATM networks

Proposes an adaptive control methodology using neural networks (NN) for the available bit rate (ABR) service class in an ATM network. A rate-based feedback controller is developed to control traffic where sources adjust their transmission rates in response to the feedback information from the network nodes. Specifically, the ATM traffic at a given node is modeled as a nonlinear discrete-time system and a one-layer neural network controller is designed to prevent congestion. Tuning methods are provided for the NN based on a delta rule to estimate the unknown ATM traffic. Mathematical analysis is given to demonstrate the stability of the closed-loop system so that a desired quality of service (QoS) can be guaranteed. No learning phase is required for the NN and initialization of the network weights is straight-forward. Simulation results are provided to justify the theoretical conclusions for a single source/single node scenario