Adaptive equalization using differential evolution

Adaptive equalization technology requires a long training sequence to update the parameters of the taps by gradient descent method step by step. In order to decrease the number of training sequence, this paper proposes an improved version of the classical differential evolution algorithm for adaptive equalizer to estimate the parameters, in which two trial vectors are created by crossover operator. The modified algorithm speeds up the convergence rate and improves the convergence precision through the evolution of multi-generation in the situation of a short training set. Compared with the traditional least mean squares (LMS) algorithm and the classical differential evolution (CDE) algorithm, the modified algorithm can switch to data transmission mode from the training mode much earlier; at the same time improve the efficiency of the transmission greatly. The simulation results have confirmed that the proposed algorithm achieves the faster convergence rate, the lower misadjustment and the less symbol error rate than the LMS algorithm and CDE algorithm in 4-PAM and 16-QAM signal systems.