An efficient Recursive Inverse adaptive filtering algorithm for channel equalization

Abstract

Recursive Inverse (RI) adaptive filtering algorithm which uses a variable step-size and the instantaneous value of the autocorrelation matrix in the coefficient update equation was proposed in [1]. The algorithm was shown to have a higher performance compared with the RLS and RRLS algorithms. In this paper, a more efficient version with lower computational complexity is presented. The performance of the algorithm has been tested in a channel equalization setting and compared with those of the Recursive Least Squares (RLS) and Stabilized Fast Transversal Recursive Least Squares (SFTRLS) algorithms in Additive White Gaussian Noise (AWGN), Additive Correlated Gaussian Noise (ACGN), Additive White Impulsive Noise (AWIN) and Additive Correlated Impulsive Noise (ACIN) environments. Simulation results show that the Fast RI algorithm performs better than RLS and requires less computations. Additionally, the performance of the Fast RI algorithm is shown to be superior to that of the SFTRLS algorithm under the same conditions.