Simulated annealing optimization in blind equalization

A novel adaptation method based on simulated annealing is introduced for blind equalization. The existing Bussgang-type blind equalization algorithms are based on the minimization of nonlinear cost functionals, utilizing a stochastic gradient approach. Due to the multimodal nature of these functionals, the gradient algorithms might converge to the wrong solution. The proposed method is based on a global optimization algorithm for functions of continuous variables within the domain of interest. The algorithm is essentially an iterative random search procedure. It permits uphill moves under the control of a probabilistic criterion, thus tending to avoid the local minima encountered. It is demonstrated, by means of computer simulations, that blind equalizers with the proposed scheme achieve global convergence in cases where steepest-descent equalizers exhibit ill-convergence