Development of adaptive channel equalization using DE

The digital channel equalizers are located in the front end of the receivers to avoid the effect of Inter-Symbol-Interference (ISI). In this paper, the equalization problem has been viewed as an optimization problem. In past the Least Mean Square Algorithm (LMS), Recursive least square (RLS), Artificial Neural Network (ANN) and Genetic Algorithm (GA) have been successfully employed for nonlinear channel equalization. The LMS, RLS and ANN techniques are derivative based and hence are chances that the parameters may fall to local minima during training. Though GA is a derivative free technique, it takes more converging time. We propose a novel equalization technique based on Differential Evolution (DE). DE is an efficient and powerful population based stochastic search technique for solving optimization problems over continuous space and hence the channel equalization performance is expected to be superior.