Channel Equalization Using a Robust Recursive Least-Squares Adaptive-Filtering Algorithm

Aimed at the existing shortcomings that robustness cannot be guaranteed for input-signals or desired-signals corrupted by impulsive noise and sudden system changes also cannot be successfully tracked in channel equalization using conventional algorithms, a new robust recursive least-squares (RLS) adaptive-filtering algorithm that uses a priori error-dependent weights is proposed. Robustness against impulsive noise is achieved by choosing the weights on the basis of the L₁ norms of the cross-correlation vector and the input-signal autocorrelation matrix. Simulation results show that the proposed algorithm offers improved robustness as well as better tracking compared to the conventional RLS and the QN adaptation algorithms.