Classification of FSO channel models using radial basis function neural networks and their ber performance with Luby transform codes

Abstract

Free Space Optical (FSO) communication systems offer a license free and cost effective access performance. FSO links can suffer from data packet corruption and erasure. Error control codes can help to mitigate turbulence induced fading and can improve the error performance of such links. 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 then investigate the error performance of FSO channels modeled as Gamma- Gamma and K distribution functions with Luby Transform encoding which are rateless codes. Simulation results are used to compare the performance of different modulation schemes with Luby Transform encoding and also to classify the appropriate distribution function for the channel model. © 2012 by IJAI (CESER Publications).