Low Cost Variable Step-Size LMS With Maximum Similarity to the Affine Projection Algorithm
The LMS algorithm is widely employed in adaptive systems due to its robustness, simplicity, and reasonable performance. However, it is well known that this algorithm suffers from a slow convergence speed when dealing with colored reference signals. Numerous variants and alternative algorithms have b...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Signal Processing |
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| Online Access: | https://ieeexplore.ieee.org/document/10345730/ |
| _version_ | 1797370498996240384 |
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| author | Miguel Ferrer Maria de Diego Alberto Gonzalez |
| author_facet | Miguel Ferrer Maria de Diego Alberto Gonzalez |
| author_sort | Miguel Ferrer |
| collection | DOAJ |
| description | The LMS algorithm is widely employed in adaptive systems due to its robustness, simplicity, and reasonable performance. However, it is well known that this algorithm suffers from a slow convergence speed when dealing with colored reference signals. Numerous variants and alternative algorithms have been proposed to address this issue, though all of them entail an increase in computational cost. Among the proposed alternatives, the affine projection algorithm stands out. This algorithm has the peculiarity of starting from <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> data vectors of the reference signal. It transforms these vectors into as many data vectors suitably normalized in energy and mutually orthogonal. In this work, we propose a version of the LMS algorithm that, similar to the affine projection algorithm, starts from <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> data vectors of the reference signal but corrects them by using only a scalar factor that functions as a convergence step. Our goal is to align the behavior of this algorithm with the behavior of the affine projection algorithm without significantly increasing the computational cost of the LMS. |
| first_indexed | 2024-03-08T18:03:26Z |
| format | Article |
| id | doaj.art-7adc3145b18b4ca48575d2214b820a96 |
| institution | Directory Open Access Journal |
| issn | 2644-1322 |
| language | English |
| last_indexed | 2024-03-08T18:03:26Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Signal Processing |
| spelling | doaj.art-7adc3145b18b4ca48575d2214b820a962024-01-02T00:02:51ZengIEEEIEEE Open Journal of Signal Processing2644-13222024-01-015829110.1109/OJSP.2023.334010610345730Low Cost Variable Step-Size LMS With Maximum Similarity to the Affine Projection AlgorithmMiguel Ferrer0https://orcid.org/0000-0002-8743-1887Maria de Diego1https://orcid.org/0000-0001-9948-3396Alberto Gonzalez2https://orcid.org/0000-0002-6984-3212Institute of Telecommunications and Multimedia Applications (iTEAM), Universitat Politècnica de València (UPV), Valencia, SpainInstitute of Telecommunications and Multimedia Applications (iTEAM), Universitat Politècnica de València (UPV), Valencia, SpainInstitute of Telecommunications and Multimedia Applications (iTEAM), Universitat Politècnica de València (UPV), Valencia, SpainThe LMS algorithm is widely employed in adaptive systems due to its robustness, simplicity, and reasonable performance. However, it is well known that this algorithm suffers from a slow convergence speed when dealing with colored reference signals. Numerous variants and alternative algorithms have been proposed to address this issue, though all of them entail an increase in computational cost. Among the proposed alternatives, the affine projection algorithm stands out. This algorithm has the peculiarity of starting from <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> data vectors of the reference signal. It transforms these vectors into as many data vectors suitably normalized in energy and mutually orthogonal. In this work, we propose a version of the LMS algorithm that, similar to the affine projection algorithm, starts from <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> data vectors of the reference signal but corrects them by using only a scalar factor that functions as a convergence step. Our goal is to align the behavior of this algorithm with the behavior of the affine projection algorithm without significantly increasing the computational cost of the LMS.https://ieeexplore.ieee.org/document/10345730/Adaptive filtersaffine projection algorithmvariable step-size |
| spellingShingle | Miguel Ferrer Maria de Diego Alberto Gonzalez Low Cost Variable Step-Size LMS With Maximum Similarity to the Affine Projection Algorithm IEEE Open Journal of Signal Processing Adaptive filters affine projection algorithm variable step-size |
| title | Low Cost Variable Step-Size LMS With Maximum Similarity to the Affine Projection Algorithm |
| title_full | Low Cost Variable Step-Size LMS With Maximum Similarity to the Affine Projection Algorithm |
| title_fullStr | Low Cost Variable Step-Size LMS With Maximum Similarity to the Affine Projection Algorithm |
| title_full_unstemmed | Low Cost Variable Step-Size LMS With Maximum Similarity to the Affine Projection Algorithm |
| title_short | Low Cost Variable Step-Size LMS With Maximum Similarity to the Affine Projection Algorithm |
| title_sort | low cost variable step size lms with maximum similarity to the affine projection algorithm |
| topic | Adaptive filters affine projection algorithm variable step-size |
| url | https://ieeexplore.ieee.org/document/10345730/ |
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