A Blind Fractionally Spaced Equalization Algorithm with Global Convergence

In this article we present a fractionally spaced extension of the SubGradient based Blind equalization Algorithm (SGBA). The basic features of the proposed algorithm are the non-linear constraint on the search vector and the selection of the weighting and step size applied to the search vector. It is proven that the algorithm is globally convergent to a perfect equalization point under the well known equalizability conditions for the fractionally spaced setting. The simulation results provided at the end of the article illustrates the relative merit of the proposed algorithm in comparison to the state of the art algorithms.