Performance of Back-propagation and Self Organizing Map Neural Equalizers for Asymmetrically Clipped Optical OFDM

This paper evaluates the performance of backpropagation (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, thereis 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 BPand 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.