Normalized p-Norm Blind Equalization Algorithm with Adaptive Momentum under Impulsive Noise Environment

Abstract

Hereby a normalized p-norm blind equalization algorithm with adaptive momentum was proposed. Normalized p-norm LMS-CMA can obtain robust convergence performance under impulsive noise environment, however, the convergence rate is slow. To further improve the performance of the normalized p-norm LMS-CMA, adaptive momentum according to the instantaneous error is designed. If the instantaneous error based on CMA criterion and DD criterion has the same sign, the momentum factor remains unchanged. Otherwise, the momentum factor is set to 0. The simulation results show that, the proposed algorithm has faster convergence rate than the normalized p-norm blind equalization algorithm, furthermore, it has robust convergence performance under impulsive noise environment.