A delayless sub-band pnlms adaptive filter for sparse channel identification

Mehdi Bekrani; Andy W.H. Khong

doi:10.1109/ICEE50131.2020.9260596

A delayless sub-band pnlms adaptive filter for sparse channel identification

Mehdi Bekrani, Andy W.H. Khong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

In sparse system identification applications such as acoustic and underwater communication, the convergence performance of conventional normalized least-mean-square (NLMS) adaptive filter is degraded due to the sparse nature of the unknown channels and the high correlation of input signals. To overcome the above issues, we propose a delayless sub-band proportionate NLMS adaptive filter with weight update process being made a function of the inverse of the approximated sub-band input correlation matrix and the estimated sub-band mean-squared error. The approximation of matrix inverse is estimated recursively using a modified matrix inversion lemma. Simulation results verify the improved performance of the proposed algorithm compared to sub-band NLMS, sub-band proportionate NLMS (PNLMS), and sparsity-aware normalized sub-band adaptive filter (NSAF) algorithms having the same sub-band structure.

Original language	English
Title of host publication	2020 28th Iranian Conference on Electrical Engineering, ICEE 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728172965
DOIs	https://doi.org/10.1109/ICEE50131.2020.9260596
Publication status	Published - Aug 4 2020
Externally published	Yes
Event	28th Iranian Conference on Electrical Engineering, ICEE 2020 - Tabriz, Iran, Islamic Republic of Duration: Aug 4 2020 → Aug 6 2020

Publication series

Name	2020 28th Iranian Conference on Electrical Engineering, ICEE 2020

Conference

Conference	28th Iranian Conference on Electrical Engineering, ICEE 2020
Country/Territory	Iran, Islamic Republic of
City	Tabriz
Period	8/4/20 → 8/6/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

ASJC Scopus Subject Areas

Energy Engineering and Power Technology
Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials

Keywords

Adaptive filter
Impulse response
Sparse channel
Sub-band structure

Access to Document

10.1109/ICEE50131.2020.9260596

Cite this

Bekrani, M., & Khong, A. W. H. (2020). A delayless sub-band pnlms adaptive filter for sparse channel identification. In 2020 28th Iranian Conference on Electrical Engineering, ICEE 2020 Article 9260596 (2020 28th Iranian Conference on Electrical Engineering, ICEE 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICEE50131.2020.9260596

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title = "A delayless sub-band pnlms adaptive filter for sparse channel identification",

abstract = "In sparse system identification applications such as acoustic and underwater communication, the convergence performance of conventional normalized least-mean-square (NLMS) adaptive filter is degraded due to the sparse nature of the unknown channels and the high correlation of input signals. To overcome the above issues, we propose a delayless sub-band proportionate NLMS adaptive filter with weight update process being made a function of the inverse of the approximated sub-band input correlation matrix and the estimated sub-band mean-squared error. The approximation of matrix inverse is estimated recursively using a modified matrix inversion lemma. Simulation results verify the improved performance of the proposed algorithm compared to sub-band NLMS, sub-band proportionate NLMS (PNLMS), and sparsity-aware normalized sub-band adaptive filter (NSAF) algorithms having the same sub-band structure.",

keywords = "Adaptive filter, Impulse response, Sparse channel, Sub-band structure",

author = "Mehdi Bekrani and Khong, \{Andy W.H.\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 28th Iranian Conference on Electrical Engineering, ICEE 2020 ; Conference date: 04-08-2020 Through 06-08-2020",

year = "2020",

month = aug,

day = "4",

doi = "10.1109/ICEE50131.2020.9260596",

language = "English",

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Bekrani, M & Khong, AWH 2020, A delayless sub-band pnlms adaptive filter for sparse channel identification. in 2020 28th Iranian Conference on Electrical Engineering, ICEE 2020., 9260596, 2020 28th Iranian Conference on Electrical Engineering, ICEE 2020, Institute of Electrical and Electronics Engineers Inc., 28th Iranian Conference on Electrical Engineering, ICEE 2020, Tabriz, Iran, Islamic Republic of, 8/4/20. https://doi.org/10.1109/ICEE50131.2020.9260596

A delayless sub-band pnlms adaptive filter for sparse channel identification. / Bekrani, Mehdi; Khong, Andy W.H.
2020 28th Iranian Conference on Electrical Engineering, ICEE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9260596 (2020 28th Iranian Conference on Electrical Engineering, ICEE 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A delayless sub-band pnlms adaptive filter for sparse channel identification

AU - Bekrani, Mehdi

AU - Khong, Andy W.H.

PY - 2020/8/4

Y1 - 2020/8/4

N2 - In sparse system identification applications such as acoustic and underwater communication, the convergence performance of conventional normalized least-mean-square (NLMS) adaptive filter is degraded due to the sparse nature of the unknown channels and the high correlation of input signals. To overcome the above issues, we propose a delayless sub-band proportionate NLMS adaptive filter with weight update process being made a function of the inverse of the approximated sub-band input correlation matrix and the estimated sub-band mean-squared error. The approximation of matrix inverse is estimated recursively using a modified matrix inversion lemma. Simulation results verify the improved performance of the proposed algorithm compared to sub-band NLMS, sub-band proportionate NLMS (PNLMS), and sparsity-aware normalized sub-band adaptive filter (NSAF) algorithms having the same sub-band structure.

AB - In sparse system identification applications such as acoustic and underwater communication, the convergence performance of conventional normalized least-mean-square (NLMS) adaptive filter is degraded due to the sparse nature of the unknown channels and the high correlation of input signals. To overcome the above issues, we propose a delayless sub-band proportionate NLMS adaptive filter with weight update process being made a function of the inverse of the approximated sub-band input correlation matrix and the estimated sub-band mean-squared error. The approximation of matrix inverse is estimated recursively using a modified matrix inversion lemma. Simulation results verify the improved performance of the proposed algorithm compared to sub-band NLMS, sub-band proportionate NLMS (PNLMS), and sparsity-aware normalized sub-band adaptive filter (NSAF) algorithms having the same sub-band structure.

KW - Adaptive filter

KW - Impulse response

KW - Sparse channel

KW - Sub-band structure

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A delayless sub-band pnlms adaptive filter for sparse channel identification

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

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