Optimal step size of the adaptive multichannel LMS algorithm for blind SIMO identification

Yiteng Huang; Jacob Benesty; Jingdong Chen

doi:10.1109/LSP.2004.842286

Optimal step size of the adaptive multichannel LMS algorithm for blind SIMO identification

Yiteng Huang, Jacob Benesty, Jingdong Chen

Research output: Contribution to journal › Article › peer-review

35 Scopus citations

Abstract

Adaptive algorithms for blindly identifying single-input multiple-output (SIMO) systems are appealing because of their computational efficiency and capability of continuously tracking a time-varying system. Adaptive multichannel least-mean-square (MCLMS) algorithms (with and without the unit-norm constraint) are analyzed, and the optimal step size is derived. A simple yet effective variable step-size MCLMS algorithm is proposed, and its performance is evaluated with simulations.

Original language	English
Pages (from-to)	173-176
Number of pages	4
Journal	IEEE Signal Processing Letters
Volume	12
Issue number	3
DOIs	https://doi.org/10.1109/LSP.2004.842286
State	Published - Mar 2005
Externally published	Yes

Keywords

Blind Channel Identification (BCI)
Least Mean Square (LMS)
Multichannel signal processing
SIMO systems
Variable step-size adaptive algorithm

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10.1109/LSP.2004.842286

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abstract = "Adaptive algorithms for blindly identifying single-input multiple-output (SIMO) systems are appealing because of their computational efficiency and capability of continuously tracking a time-varying system. Adaptive multichannel least-mean-square (MCLMS) algorithms (with and without the unit-norm constraint) are analyzed, and the optimal step size is derived. A simple yet effective variable step-size MCLMS algorithm is proposed, and its performance is evaluated with simulations.",

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KW - Least Mean Square (LMS)

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Optimal step size of the adaptive multichannel LMS algorithm for blind SIMO identification

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Keywords

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