Improved Blind Equalization Scheme Using Variable Step Size Constant Modulus Algorithm

The blind equalizer relies on the knowledge of signal’s constellation and its statistics to perform equalization. The major drawback is that the blind equalizer will typically take a longer time to converge as compared to a trained equalizer. Variable Step-Size LMS (VSLMS) algorithms have been introduced to optimize the speed and steady-state error. The relationships between the a priori and a posteriori error signals have been used to quickly and easily characterize the stability and robustness of the given adaptive algorithms. Our proposed algorithm uses both variable step size and a posteriori updates to increase the speed of convergence while reducing the trade off between the convergence speed and steady state error.