A hybrid variable step-size adaptive blind equalization algorithm for QAM signals

We develop an adaptive decision-feedback equalization algorithm that combines blind adaptation and decision-directed LMS in a dynamic manner according to the amount of equalizer output error. By observing how the mean-square blind equalization error depends on the adaptation step size, we obtain a way of continuously varying the adaptation speed of the overall algorithm with the equalizer output error as well as a way to shift the relative emphasis between blind and decision-directed LMS operation. We also describe the way to estimate the amount of equalizer output error in the algorithm. Simulation results show that the proposed algorithm can achieve faster convergence at a lower complexity than some recently proposed hybrid adaptation algorithms