A fast blind equalization method based on subgradient projections

Abstract

A novel blind equalization method based on a subgradient search over a convex cost surface is proposed. This is an alternative to the existing iterative blind equalization approaches such as the Constant Modulus Algorithm (CMA) which mostly suffer from the convergence problems caused by their non-convex cost functions. The proposed method is an iterative algorithm, for both real and complex constellations, with a very simple update rule that minimizes the l(infinity) norm of the equalizer output under a linear constraint on the equalizer coefficients. The algorithm has a nice convergence behavior attributed to the convex l(infinity) cost surface. Examples are provided to illustrate the algorithm's performance.