Nonlinear adaptive channel equalization using genetic algorithms

Abstract

Nonlinear adaptive channel equalization is a well-documented problem. Equalizers based on the complex decision feedback recurrent neural network (CDFRNN) have been intensively studied to address this problem. However, when trained with conventional training algorithms like the real time recurrent learning (RTRL) technique, the equalizer suffers from low convergence speed, requiring very long training sequence to achieve proper performance. In this work, we propose a new approach to equalize nonlinear channels using genetic algorithms. The proposed Volterra decision feedback genetic algorithm (VDFGA) uses a genetic optimization strategy to estimate Volterra kernels in order to model the inverse of the channel response. Simulation results show very high convergence speed, which allowed to achieve interesting bit error rate (BER) using relatively short training symbols, when considering only 8-bits long coded weights.