Nonlinear channel blind equalization using hybrid genetic algorithm with simulated annealing

In this paper, a hybrid genetic algorithm, which merges a genetic algorithm with simulated annealing, is presented to solve nonlinear channel blind equalization problems. The equalization of nonlinear channels is more complicated than linear channels, but it is of more practical use in real world environments. The proposed hybrid genetic algorithm with simulated annealing is used to estimate the output states of a nonlinear channel, based on the Bayesian likelihood fitness function, instead of the channel parameters. By using the desired channel states derived from these estimated output states of the nonlinear channel, the Bayesian equalizer is implemented to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with those of a conventional genetic algorithm (GA) and a simplex GA. In particular, we observe a relatively high accuracy and fast convergence of the method.