Artificial neural net applications in telecommunication systems

The artificial neural net development has had something of a renaissance in the last decade with an impressive range of application areas. From the viewpoint of telecommunication networks and systems, an increasing number of studies can be observed in recent literature dealing with proposed applications of neural nets in telecommunication environments, such as connection admission control in broadband networks, the control of high-speed interconnection networks, channel allocation in cellular mobile systems, adaptive routing, etc. These proposed applications largely use three main neural net classes: feed-forward nets with backpropagation learning, Hopfield feedback nets, and selforganising neural nets. In this paper, we first give an overview of neural net classes and their main properties, and then present a review of applications in telecommunication systems, where attention is devoted to numerical aspects such as the convergence property and learning speed of the proposed neural nets.

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