Using RBF Neural Networks and Kullback-Leibler distance to classify channel models in Free Space Optics

Abstract

Free Space Optical (FSO) communication systems offer a license free and cost effective access performance. FSO systems provide virtually unlimited bandwidth. Since the laser beams used in these systems are spatially confined, the links are very secure. However FSO links perform well only in clear weather conditions. Clouds, fog, aerosols, and turbulence drastically affect the performance of FSO systems and lead to fluctuations in both the intensity and phase of the received signal. FSO links can suffer from data packet corruption and erasure. Various statistical models have been proposed to describe the atmospheric turbulence channels. The choice of the appropriate model for varying level of turbulence is dependent on the atmospheric parameters. In this paper we classify the channels using Radial Basis Function Neural Networks to decide the best fit. We also use Kullback-Leibler distance as a measure between the reference distribution and the distribution of observed data.