An Efficient Filtering Algorithm against Impulse Noise in Communication Systems

An Efficient Filtering Algorithm against Impulse Noise in Communication Systems

The kernel adaptive filter (KAF), which processes data in the reproducing kernel Hilbert space (RKHS), can improve the performance of conventional adaptive filters in nonlinear systems. However, the presence of impulse noise can seriously degrade the performance of KAF. In this paper, we propose a kernel modified-sign least-mean-square algorithm (KMSLMS) to mitigate the impact of impulse noise in communication systems. Moreover, we apply the nearest-instance-centroid estimation (NICE) algorithm to reduce the computational complexity of our KMSLMS algorithm, called the NICE-KMSLMS algorithm. Finally, computer simulations were used to evaluate the effectiveness of our proposed method. Compared with the conventional kernel least-mean-square algorithm (KLMS), our proposed method can improve the testing mean-squared error (MSE) by 2.32 dB and 7.39 dB for the nonlinear channel equalization and Mackey-Glass chaotic time series prediction problems, respectively. Furthermore, the testing MSE degradation caused by combining the NICE algorithm with our KMSLMS algorithm is negligible but can save about 55% computational cost in terms of the required mean size.