Noisy Chaotic Neural Networks With Variable Thresholds for the Frequency Assignment Problem in Satellite Communications

Abstract

We propose a novel approach, i.e., a noisy chaotic neural network with variable thresholds (NCNN-VT), to solve the frequency assignment problem in satellite communications. The objective of this NP-complete optimization problem is to minimize cochannel interference between two satellite systems by rearranging frequency assignments. The NCNN-VT model consists N times M of noisy chaotic neurons for an N-carrier M-segment problem. The NCNN-VT facilitates the interference minimization by mapping the objective to variable thresholds (biases) of the neurons. The performance of the NCNN-VT is demonstrated by solving a set of benchmark problems and randomly generated test instances. The NCNN-VT achieves better solutions, i.e., smaller interference with much lower computation cost compared to existing algorithms.