A NEURAL-NETWORK CONTROLLER FOR CONGESTION CONTROL IN ATM MULTIPLEXERS

This paper presents an adaptive approach to the problem of congestion control arising at the User-to Network Interface (UNI) of an ATM multi plexer. We view the ATM multiplexer as a non-linear stochastic system whose dynamics are ill-defined. Rear-time measurements of the arrival rate process and the queueing process, are used to identify, and minim ize congestion episodes. The performance of the system is evaluated us ing a performance-index function which is a quantitive measure of ''ho w well'' the system is performing. A three-layers backpropagation neur al network controller generates a signal that attempts to minimize con gestion without degrading the quality of the traffic, During periods o f buffer over-load the control signal, adaptively, modulates the arriv al process such that its peak-rate is throttled-down. As soon as conge stion is terminated, the control signal is adjusted such that the codi ng rates are restored back to their original values. Adaptability is a chieved by continuously adjusting the weights of the neural network co ntroller such that the performance of the system, measured by its perf ormance index function, is maximized over a certain optimization perio d. The performance index function is defined in terms of two main obje ctives: (1) to minimize the cell loss rate (CLR), i.e., minimize conge stion episodes, and (2) to maintain the quality of the video/audio tra ffic by maintaining its original source coding rate. The neural networ k learning process can be viewed as a specialized form of reinforcemen t learning in the sense that the control signal is reinforced if it te nds to maximize the performance index function. Performance evaluation results prove that this approach is effective in controlling congesti on while maintaining the quality of the traffic.