Neural Network Control of Communications Systems

Abstract

Neural networks appear well suited to applications in the control of communications systems for two particular reasons: adaptivity and high speed. This paper describes application of neural networks to two problems, the admission control problem and the switch control problem, which exploit the adaptivity and speed property, respectively. The admission control problem is the selective admission of a set of calls from a number of inhomogeneous call classes, which may have widely differing characteristics as to their rate and variability of traffic, onto a network. It is usually unknown in advance which combinations of calls can be simultaneously accepted so as to ensure satisfactory performance. The approach adopted here is that key network performance parameters are observed while carrying various combinations of calls, and their relationship is learned by a neural network structure. The neural network model chosen has the ability to interpolate or extrapolate from the past-experienced results, it also has the ability to adapt to new and changing conditions. The switch control problem is the service policy used by a switch controller in transmitting packets. In a crossbar switch with input queueing, significant loss of throughput can occur when head-of-line service order is employed. A solution to this problem can be based on an algorithm which maximizes throughput. However since this solution is typically required in less than one microsecond, software implementation of the policy is infeasible. We will carry out an analysis of the benefits of such a policy, describe some existing proposed schemes for its implementation, and propose a further scheme that provides this submicrosecond optimization. © 1994 IEEE