A novel radius-adjusted approach for blind adaptive equalization

Abstract

A new radius-adjusted approach for blind adaptive equalization for quadrature amplitude modulation (QAM) signals is introduced. Static circular contours are defined around an estimated symbol point in a QAM signal constellation, which creates regions that can be mapped to adaptation phases. The equalizer tap update consists of a linearly weighted sum of adaptation criteria that is scaled by a variable step size. Each region corresponds to a fixed step size and weighting factor, which creates a time-varying tap update based on the equalizer output radius. Two new algorithms are proposed based on this new approach and the multimodulus algorithm (MMA). The first algorithm trades off MMA and constellation-matched errors to reduce the time-to-convergence and mean-squared error (MSE), while the second trades off MMA and decision-directed errors to achieve reliable transfer between error modes and to obtain low MSE. A method to tune the proposed algorithms is developed based on statistics of the radius. The proposed algorithms are compared with related blind algorithms, and simulation results confirm that the proposed algorithms lead to enhanced performance.