A Radial Basis Function Network for Adaptive Channel Equalization in Coherent Optical OFDM Systems

Artificial neural network based equalizers can be used for equalization in coherent optical OFDM systems. The artificial neural network based multilayer layer perceptron is a feed-forward network consists of one hidden layer with one or more hidden nodes between its input and output layers and can be trained by using back propagation algorithm. However, this algorithm suffers from slow convergence rate, depending on the size of artificial neural network. The training function can update the weights and the bias values according to the resilient back-propagation algorithm, which is computationally more efficient than other training algorithms, and it performs an approximation to the global minimization. It has been seen that an optimal equalizer based on maximum a-posterior probability (MAP) criterion can be implemented using Radial basis function network. In a RBF equalizer, centres are fixed using K-mean clustering and weights are trained using LMS algorithm. RBF equalizer can mitigate ISI interference effectively providing minimum BER plot. In this paper A Radial Basis Function network for adaptive channel equalization in Coherent Optical OFDM Systems has been presented its result has been compared with MLP based artificial neural network.