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

The introduction of artificial neural networks to realtime 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 combinatorial 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.