A Variable Step Size Normalized Least-Mean-Square Algorithm Based on Data Reuse

• Main Authors: Benesty, J. (Author), Ciochină, S. (Author), Paleologu, C. (Author), Rusu, A.-G (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Acoustic echo cancelation; Acoustic echo cancellation; acoustic echo cancellation (AEC); Adaptive filtering; adaptive filters; Adaptive filters; data reuse; Data reuse; Echo suppression; Impulse response; Least mean squares; Loudspeakers; Normalized least mean squares algorithms; normalized least-meansquare (NLMS) algorithm; Normalized least-meansquare algorithm; Parameter estimation; Performance; Reusability; Step size; Variable step size; variable step size (VSS)
• Online Access: View Fulltext in Publisher

 The principal issue in acoustic echo cancellation (AEC) is to estimate the impulse response between the loudspeaker and microphone of a hands-free communication device. This application can be addressed as a system identification problem, which can be solved by using an adaptive filter. The most common one for AEC is the normalized least-mean-square (NLMS) algorithm. It is known that the overall performance of this algorithm is controlled by the value of its normalized step size parameter. In order to obtain a proper compromise between the main performance criteria (e.g., convergence rate/tracking versus accuracy/robustness), this specific term of the NLMS algorithm can be further controlled and designed as a variable parameter. This represents the main motivation behind the development of variable step size algorithms. In this paper, we propose a variable step size NLMS (VSS-NLMS) algorithm that exploits the data reuse mechanism, which aims to improve the convergence rate/tracking of the algorithm by reusing the same set of data (i.e., the input and reference signals) several times. Nevertheless, we involved an equivalent version of the data reuse NLMS, which provides the convergence modes of the algorithm. Based on this approach, a sequence of normalized step sizes can be a priori scheduled, which is advantageous in terms of the computational complexity. The simulation results in the context of AEC supported the good performance features of the proposed VSS-NLMS algorithm. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.