A neural network driven solution to a channel assignment problem in wireless telephony

The introduction of artificial neural networks to real-time channel assignment techniques in cellular mobile radio systems has met with varying degrees of success. We have translated a dynamic channel assignment algorithm into a combinational optimization problem which can be solved on a suitably modified Hopfield neural network. Here we prove that the conventional and our neural network based approaches are equivalent. This neural architecture can be further extended to include a more powerful algorithm that incorporates channel rearrangements between cells. Extensive simulations show that our approach outperforms some other neural models considered elsewhere in the literature.