A maximum neural network with self-feedbacks for channel assignment in cellular mobile systems

Abstract

The maximum neural network (MNN) with self-feedbacks for the channel assignment problem (CAP) is proposed. The CAP is one of the extremely important problems in cellular mobile systems. The CAP is to assign a channel to each call in order to minimize the interference and use available channels efficiently. Funabiki et al. (2000) have proposed the hysteresis binary neuron model for the CAP and it can find lower bound solutions for well-known benchmark problems. In order to avoid converging to a local minimum, this model introduces the hill-climbing term and the omega function. Although these methodologies are effective to escape from a local minimum, they need to adjust many parameters. In this paper, the MNN with self-feedbacks is proposed in order to reduce parameters. Our proposal is applied to the CAP, and it is compared with the hysteresis binary neuron model. Our model can find the lower bound solutions in all of the benchmark problems and the average iteration step decreases by 55.5[%].