Fast Converging Algorithm for Blind Equalization with Gaussian and Impulsive Noises

This paper proposes a blind equalization algorithm for dispersive wireless communication systems that employ high throughput quadrature amplitude modulation signals under both Gaussian and impulsive noise environments. A novel cost function that combines the modulus match error function with the negative Gaussian kernel function is established to efficiently obtain the weight vector associated with the blind equalizer. Some preferable properties of the novel cost function are presented. Intensive studies show that the proposed cost function efficiently reduces the maladjustment caused by the modulus mismatch error and efficiently suppresses the negative influence resulting from large errors. Moreover, an efficient successive approximation method for minimizing the established cost function is proposed for fast searching of the optimal weight vector. Very importantly, it is proved that the proposed successive approximation method possesses superlinear convergence. Finally, extensive simulations are provided to demonstrate that the proposed blind equalizer has better performances than the existing methods under both Gaussian and impulsive noise circumstances in terms of equalization quality and equalization efficiency.