An Improved Blind Equalization Algorithm Based on Recurrent Neural Networks

A novel fast convergence blind equalization algorithm based on recurrent neural network is proposed. Four-order statistics of the observation signals are used to calculate the cost function in order to simplify the complexity of the equalization system. Real-time recursion training algorithm is used to dynamically adjust the system parameters. The blind equalization algorithm is "equanimous" and the characteristic of convergence is not influenced by distortion of channel, it is fit for equalizing deep attenuation channel. Simulation results show that the algorithm has good performance on convergence speed and compensating for inter-symbol interference created by multi-path within non-linear channel.