A fractionally spaced blind equalization algorithm with global convergence

Abstract

Two different fractionally spaced extensions of the SubGradient based Blind equalization Algorithm (SGBA) are provided. The first one is the direct extension of the linearly constrained SGBA for the symbol spaced setting. The second extension is the weighted and the 2-norm constrained fractionally spaced SGBA (FS-SGBA) algorithm. It is proven that the latter algorithm is globally convergent to a perfect equalization point under the well-known equalizability conditions for the fractionally spaced setting. The simulation results provided illustrates the relative merit of the proposed algorithm in comparison to the state of the art algorithms.