Fast and low complexity blind equalization via subgradient projections

We propose a novel blind equalization method based on subgradient search over a convex cost surface. This is an alternative to the existing iterative blind equalization approaches such as the Constant Modulus Algorithm (CMA), which often suffer from the convergence problems caused by their nonconvex cost functions. The proposed method is an iterative algorithm called SubGradient based Blind Algorithm (SGBA) for both real and complex constellations, with a very simple update rule. It is based on the minimization of the l_∞ norm of the equalizer output under a linear constraint on the equalizer coefficients using subgradient iterations. The algorithm has a nice convergence behavior attributed to the convex l_∞ cost surface as well as the step size selection rules associated with the subgradient search. We illustrate the performance of the algorithm using examples with both complex and real constellations, where we show that the proposed algorithm´s convergence is less sensitive to initial point selection, and a fast convergence behavior can be achieved with a judicious selection of step sizes. Furthermore, the amount of data required for the training of the equalizer is significantly lower than most of the existing schemes.