New efficient LMS adaptive filtering algorithm

Sau Gee Chen; Yung An Kao; Kwung Yee Tsai

New efficient LMS adaptive filtering algorithm

Sau Gee Chen^*, Yung An Kao, Kwung Yee Tsai

^*Corresponding author for this work

National Yang Ming Chiao Tung University

Research output: Contribution to conference › Conference Paper › peer-review

Abstract

A new LMS adaptive filtering algorithm is proposed. The algorithm has comparable performance to the direct-form LMS algorithm (DLMS), while costs N/2-1 less multiplications at the expense of N/2+3 more additions than DLMS algorithm. For coefficient update, the new algorithm needs one additional coefficient estimation. Further, the algorithm is combined with LMS sign algorithm (SA), signed regressor algorithm (SRA) and zero forcing (ZFA) algorithm for further complexity reduction. Simulation results confirm with theoretical analysis that the new algorithm and its SA, SRA and ZFA versions converge as fast as their counter DLMS algorithms, while maintain comparable performances.

Original language	English
Pages	644-648
Number of pages	5
State	Published - 1995
Externally published	Yes
Event	Proceedings of the 1994 IEEE Region 10's 9th Annual International Conference (TENCON'94). Part 1 (of 2) - Singapore, Singapore Duration: 22 08 1994 → 26 08 1994

Conference

Conference	Proceedings of the 1994 IEEE Region 10's 9th Annual International Conference (TENCON'94). Part 1 (of 2)
City	Singapore, Singapore
Period	22/08/94 → 26/08/94

Cite this

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title = "New efficient LMS adaptive filtering algorithm",

abstract = "A new LMS adaptive filtering algorithm is proposed. The algorithm has comparable performance to the direct-form LMS algorithm (DLMS), while costs N/2-1 less multiplications at the expense of N/2+3 more additions than DLMS algorithm. For coefficient update, the new algorithm needs one additional coefficient estimation. Further, the algorithm is combined with LMS sign algorithm (SA), signed regressor algorithm (SRA) and zero forcing (ZFA) algorithm for further complexity reduction. Simulation results confirm with theoretical analysis that the new algorithm and its SA, SRA and ZFA versions converge as fast as their counter DLMS algorithms, while maintain comparable performances.",

author = "Chen, {Sau Gee} and Kao, {Yung An} and Tsai, {Kwung Yee}",

year = "1995",

language = "英语",

pages = "644--648",

note = "Proceedings of the 1994 IEEE Region 10's 9th Annual International Conference (TENCON'94). Part 1 (of 2) ; Conference date: 22-08-1994 Through 26-08-1994",

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AU - Chen, Sau Gee

AU - Kao, Yung An

AU - Tsai, Kwung Yee

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N2 - A new LMS adaptive filtering algorithm is proposed. The algorithm has comparable performance to the direct-form LMS algorithm (DLMS), while costs N/2-1 less multiplications at the expense of N/2+3 more additions than DLMS algorithm. For coefficient update, the new algorithm needs one additional coefficient estimation. Further, the algorithm is combined with LMS sign algorithm (SA), signed regressor algorithm (SRA) and zero forcing (ZFA) algorithm for further complexity reduction. Simulation results confirm with theoretical analysis that the new algorithm and its SA, SRA and ZFA versions converge as fast as their counter DLMS algorithms, while maintain comparable performances.

AB - A new LMS adaptive filtering algorithm is proposed. The algorithm has comparable performance to the direct-form LMS algorithm (DLMS), while costs N/2-1 less multiplications at the expense of N/2+3 more additions than DLMS algorithm. For coefficient update, the new algorithm needs one additional coefficient estimation. Further, the algorithm is combined with LMS sign algorithm (SA), signed regressor algorithm (SRA) and zero forcing (ZFA) algorithm for further complexity reduction. Simulation results confirm with theoretical analysis that the new algorithm and its SA, SRA and ZFA versions converge as fast as their counter DLMS algorithms, while maintain comparable performances.

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M3 - 论文

AN - SCOPUS:0029231549

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T2 - Proceedings of the 1994 IEEE Region 10's 9th Annual International Conference (TENCON'94). Part 1 (of 2)

Y2 - 22 August 1994 through 26 August 1994

ER -

New efficient LMS adaptive filtering algorithm

Abstract

Conference

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