Low-Complexity Decorrelation NLMS Algorithms: Performance Analysis and AEC Application

In the traditional decorrelation normalized least-mean-square (D-NLMS) algorithm, high computational complexity is mainly caused by finding the decorrelated-vector. To address this issue, this article proposes a low-complexity implementation approach, which cleverly utilizes the periodic update of t...

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Veröffentlicht in:IEEE transactions on signal processing Jg. 68; S. 6621 - 6632
Hauptverfasser: Zhang, Sheng, Zhang, Jiashu, So, Hing Cheung
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptive filter
Algorithms
Approximation algorithms
colored inputs
Complexity
Computational modeling
Convergence
Decorrelation
low complexity
Optimized production technology
Signal processing algorithms
Steady-state
System identification
ISSN:1053-587X, 1941-0476
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Zusammenfassung:In the traditional decorrelation normalized least-mean-square (D-NLMS) algorithm, high computational complexity is mainly caused by finding the decorrelated-vector. To address this issue, this article proposes a low-complexity implementation approach, which cleverly utilizes the periodic update of the decorrelation parameters and delay characteristics of the decorrelated-vector. We firstly develop two low-complexity decorrelation algorithms, (i) fast D-NLMS (FD-NLMS) and (ii) approximate FD-NLMS (AFD-NLMS) which is an approximate version of the first algorithm with even smaller computational requirement. Theoretical performance of the FD-NLMS scheme is also derived. To further obtain low steady-state error in the acoustic echo cancellation (AEC) application, separated-decorrelation AEC structure and robust step-size schemes are designed, resulting in two improved algorithms, namely, fast separated-decorrelation NLMS (FSD-NLMS) and approximate FSD-NLMS (AFSD-NLMS). Finally, extensive simulation study on system identification and AEC is undertaken to verify the efficiency of the proposed methods.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2020.3039595