SOLVING THE RADIO LINK FREQUENCY ASSIGNMENT PROBLEM WITH BOLTZMANN MACHINE

Abstract

Neural network models that became well known and popular in the 80's have been successfully applied to solve tasks in several domains. These systems seem to offer fast and robust solutions for several difficult problems. The comparison of the results often shows similar achievements for neural networks and conventional methods. There is nothing surprising in it, if we consider that the different types of artificial neural systems accomplish the same or similar procedures as the different search algorithms and other methods. Most of the neural networks realize a modification of previously known algorithms on an intrinsic parallel system. Although the underlying methods are similar. the parallel structure and the nonlinear processing elements offer us a new, more efficient method. In this paper we present how to map a constraint satisfaction problem to achieve fast, optimal or near optimal solution. The application task, which has been solved, is the Radio Link Frequency Assignment Problem (RLFAP). In this problem we have to assign frequencies from a finite domain to several radio connections in such a way that the result should meet numerous constraints. The first section briefly describes the neural network model we have used to solve the problem. The second part introduces the RFLAP task in more detail. In the following two sections first we show a possible method to map the problem to the neural network and after this we present and evaluate the achieved results. In the fifth part we finish the paper with some conclusions.