Recursive Complex Extreme Learning Machine with Widely Linear Processing for Nonlinear Channel Equalizer
Recently, a new learning algorithm for the feedforward neural network named the complex extreme learning machine (C-ELM) which can give better performance than traditional tuning-based learning methods for feedforward neural networks in terms of generalization and learning speed has been proposed by Huang et al. In this paper, we propose a new widely linear recursive C-ELM algorithm for nonlinear channel equalizer. The proposed algorithm improves its performance especially in case of real valued modulation such as BPSK and PAM. The computer simulation results demonstrate the improvement in performance achievable with the proposed equalization algorithm.
KeywordsHide Neuron Feedforward Neural Network Linear Processing Recursive Less Square Channel Equalization
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