A Novel Variable Step Size NLMS Algorithm Based on the Power Estimate of the System Noise

TL;DR

This paper introduces a variable step size NLMS algorithm that estimates system noise power to improve convergence speed and steady-state accuracy in system identification and acoustic echo cancellation tasks.

Contribution

It proposes a new VSS NLMS algorithm that uses power estimates of system noise and input signals to enhance performance over existing methods.

Findings

Lower steady-state misalignment compared to other VSS algorithms.

Effective in system identification and acoustic echo cancellation.

Validated through simulations with Gaussian white input signals.

Abstract

To overcome the tradeoff of the conventional normalized least mean square (NLMS) algorithm between fast convergence rate and low steady-state misalignment, this paper proposes a variable step size (VSS) NLMS algorithm by devising a new strategy to update the step size. In this strategy, the input signal power and the cross-correlation between the input signal and the error signal are used to estimate the true tracking error power, reducing the effect of the system noise on the algorithm performance. Moreover, the steady-state performances of the algorithm are provided for Gaussian white input signal and are verified by simulations. Finally, simulation results in the context of the system identification and acoustic echo cancellation (AEC) have demonstrated that the proposed algorithm has lower steady-state misalignment than other VSS algorithms.