Selective coefficient update of gradient-based adaptive algorithms

Abstract

One common approach to reducing the computational overhead of the normalized LMS (NLMS) algorithm is to update a subset of the adaptive filter coefficients. It is known that the mean square error (MSE) is not equally sensitive to the variations of the coefficients. Accordingly, the choice of the coefficients to be updated becomes crucial. On this basis, we propose an algorithm that belongs to the same family but selects at each iteration a specific subset of the coefficients that will result in the largest reduction in the performance error. The proposed algorithm reduces the complexity of the NLMS algorithm, as do the current algorithms from the same family, while maintaining a performance close to the full update NLMS algorithm specifically for correlated inputs.