Performance comparison of adaptive channel equalizers using different variants of differential evolution

In this paper, Differential Evolution (DE) based channel equalization is proposed and an in-depth comparison of the performance of different variants of DE is made. Adaptive equalization involves training of parameters such that the transmitted data is faithfully received. The equalization task is viewed as an optimization problem where the mean square error between the delayed transmitted signal and the equalizer output is minimized iteratively. In this paper, the equalizer coefficients are achieved using different variants of DE and the performance is compared in terms of convergence rate, optimality of solution and Bit Error Rate. Thus, the DE-based learning technique is an efficient method for adaptive nonlinear channel equalization