Channel Equalization Using Complex Extreme Learning Machine with RBF Kernels

This paper studies the performance of extreme learning machine with complex-valued radial basis function (ELM-CRBF) in the channel equalization applications. Comparing with complex minimal resource allocation network (CMRAN), complex radial basis function (CRBF) network and Bayesian equalizers, the simulation results show that ELM-CRBF equalizer is superior in terms of symbol error rate (SER) and learning speed.

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