Performance of back-propagation and self organizing map neural equalizers for asymmetrically clipped optical OFDM

Abstract

This paper evaluates the performance of back-propagation (BP) and self organizing map (SOM) equalizer for indoor asymmetrically clipped optical OFDM (ACO-OFDM) wireless systems. Although in OFDM, parallel transmission and cyclic prefix addition improve the communication efficiency, there is still performance degradation due to inter-symbol interference (ISI) in dispersive channels. Simulation results indicate that BP multilayer perceptron and SOM neural equalizers enhance the bit error rate performance of ACO-OFDM systems in diffused channels for high signal to noise ratio. Moreover, proposed BP and SOM equalization require only 0.05 and 0.005 percent of ACO-OFDM symbols for training in a sec respectively while in single-tap equalization channel state information is necessary at the receiver.