New data-reusing LMS algorithms for improved convergence

Bernard A. Schnaufer; W. Kenneth Jenkins

New data-reusing LMS algorithms for improved convergence

Bernard A. Schnaufer, W. Kenneth Jenkins

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

Abstract

In this paper a geometric framework is adopted which is used to clearly elucidate the operation of and relationships between the LMS, DR-LMS, and NLMS algorithms. This geometrical framework facilitates the proof of an analytical result which explains the superior convergence rate performance of the NLMS algorithm. A new class of computationally efficient data-reusing algorithms is then introduced which provides significant convergence rate improvement over the DR-LMS algorithm. The improved performance is verified with simulations.

Original language	English (US)
Title of host publication	Conference Record of the Asilomar Conference of Signals, Systems & Computers
Publisher	Publ by IEEE
Pages	1584-1588
Number of pages	5
ISBN (Print)	0818641207
State	Published - 1993
Event	Proceedings of the 27th Asilomar Conference on Signals, Systems & Computers - Pacific Grove, CA, USA Duration: Nov 1 1993 → Nov 3 1993

Publication series

Name	Conference Record of the Asilomar Conference of Signals, Systems & Computers
Volume	2
ISSN (Print)	1058-6393

Other

Other	Proceedings of the 27th Asilomar Conference on Signals, Systems & Computers
City	Pacific Grove, CA, USA
Period	11/1/93 → 11/3/93

All Science Journal Classification (ASJC) codes

Signal Processing
Computer Networks and Communications

Cite this

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title = "New data-reusing LMS algorithms for improved convergence",

abstract = "In this paper a geometric framework is adopted which is used to clearly elucidate the operation of and relationships between the LMS, DR-LMS, and NLMS algorithms. This geometrical framework facilitates the proof of an analytical result which explains the superior convergence rate performance of the NLMS algorithm. A new class of computationally efficient data-reusing algorithms is then introduced which provides significant convergence rate improvement over the DR-LMS algorithm. The improved performance is verified with simulations.",

author = "Schnaufer, {Bernard A.} and Jenkins, {W. Kenneth}",

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pages = "1584--1588",

booktitle = "Conference Record of the Asilomar Conference of Signals, Systems & Computers",

note = "Proceedings of the 27th Asilomar Conference on Signals, Systems & Computers ; Conference date: 01-11-1993 Through 03-11-1993",

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Schnaufer, BA & Jenkins, WK 1993, New data-reusing LMS algorithms for improved convergence. in Conference Record of the Asilomar Conference of Signals, Systems & Computers. Conference Record of the Asilomar Conference of Signals, Systems & Computers, vol. 2, Publ by IEEE, pp. 1584-1588, Proceedings of the 27th Asilomar Conference on Signals, Systems & Computers, Pacific Grove, CA, USA, 11/1/93.

New data-reusing LMS algorithms for improved convergence. / Schnaufer, Bernard A.; Jenkins, W. Kenneth.
Conference Record of the Asilomar Conference of Signals, Systems & Computers. Publ by IEEE, 1993. p. 1584-1588 (Conference Record of the Asilomar Conference of Signals, Systems & Computers; Vol. 2).

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

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AB - In this paper a geometric framework is adopted which is used to clearly elucidate the operation of and relationships between the LMS, DR-LMS, and NLMS algorithms. This geometrical framework facilitates the proof of an analytical result which explains the superior convergence rate performance of the NLMS algorithm. A new class of computationally efficient data-reusing algorithms is then introduced which provides significant convergence rate improvement over the DR-LMS algorithm. The improved performance is verified with simulations.

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BT - Conference Record of the Asilomar Conference of Signals, Systems & Computers

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T2 - Proceedings of the 27th Asilomar Conference on Signals, Systems & Computers

Y2 - 1 November 1993 through 3 November 1993

ER -

New data-reusing LMS algorithms for improved convergence

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

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