Abstract- 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
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