A Variable Step-size NLMS Algorithm Employing Partial Update Schemes for Echo Cancellation

Today, with increase in the demand for higher quality communication, a kind of long adaptive filter is frequently encountered in practical application, such as the acoustic echo cancellation. Increase of adaptive filter length from decades to hundreds or thousands causes the conventional adaptive algorithms encounter new challenges. Therefore, a new variable step-size normalized least-mean-square algorithm combined with Partial update is proposed and its performances are investigated through simulations. The proposed step size method takes into account the instantaneous value of the output error and provides a trade-off between the convergence rate and the steady-state coefficient error. In order to deal with this obstacle that the large number of filter coefficients diminishes the usefulness of the adaptive filtering algorithm owing to increased complexity, the new algorithm employing tap-selection partial update schemes only updates subset of the filter coefficients that correspond to the largest magnitude elements of the regression vector. Simulation results of such applications in acoustic echo cancellation verify that the proposed algorithm achieves higher rate of convergence and brings significant computation savings compared to the NLMS algorithm.