Name
Abstract | ||
|---|---|---|
Many real systems have inherently some type of sparsity. Recently, the feature least-mean square (F-LMS) has been proposed to exploit hidden sparsity. Unlike the existing algorithms, the F-LMS algorithm performs a linear combination of the adaptive coefficients to reveal and then exploit the hidden sparsity. However, many systems have also plain besides hidden sparsity, and the F-LMS algorithm is not able to exploit the former. In this paper, we propose a new algorithm, named simple sparsity-aware F-LMS (SSF-LMS) algorithm, that is capable of exploiting both kinds of sparsity simultaneously. The hidden sparsity is exploited just like in the F-LMS algorithm, whereas the plain sparsity is exploited by means of the discard function applied to the filter coefficients. By doing so, the proposed SSFLMS algorithm not only outperforms the F-LMS algorithm when plain sparsity is also observed, but also requires fewer arithmetic operations. Numerical results show that the proposed algorithm has faster speed of convergence and reaches lower steady-state mean-squared error (MSE) than the F-LMS and classical algorithms, when the system has plain and hidden sparsity. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.23919/EUSIPCO.2019.8902960 | 2019 27th European Signal Processing Conference (EUSIPCO) |
Keywords | Field | DocType |
adaptive filtering,LMS algorithm,feature matrix,discard function,sparsity | Least mean squares filter,Convergence (routing),Linear combination,Computer science,Algorithm,Exploit,Adaptive filter,Feature matrix,Real systems,Filter design | Conference |
ISSN | ISBN | Citations |
2219-5491 | 978-1-5386-7300-3 | 0 |
PageRank | References | Authors |
0.34 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriel S. Chaves | 1 | 0 | 0.34 |
| Markus V. S. Lima | 2 | 57 | 11.75 |
| Hamed Yazdanpanah | 3 | 6 | 6.19 |
| Paulo S. R. Diniz | 4 | 247 | 38.72 |
| Ferreira, T.N. | 5 | 2 | 2.51 |