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Proceedings Paper

Turbo LMS algorithm: Supercharger meets adaptive filter
Author(s): Uwe Meyer-Baese
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Paper Abstract

Adaptive digital filters (ADFs) are, in general, the most sophisticated and resource intensive components of modern digital signal processing (DSP) and communication systems. Improvements in performance or the complexity of ADFs can have a significant impact on the overall size, speed, and power properties of a complete system. The least mean square (LMS) algorithm is a popular algorithm for coefficient adaptation in ADF because it is robust, easy to implement, and a close approximation to the optimal Wiener-Hopf least mean square solution. The main weakness of the LMS algorithm is the slow convergence, especially for non Markov-1 colored noise input signals with high eigenvalue ratios (EVRs). Since its introduction in 1993, the turbo (supercharge) principle has been successfully applied in error correction decoding and has become very popular because it reaches the theoretical limits of communication capacity predicted 5 decades ago by Shannon. The turbo principle applied to LMS ADF is analogous to the turbo principle used for error correction decoders: First, an "interleaver" is used to minimize crosscorrelation, secondly, an iterative improvement which uses the same data set several times is implemented using the standard LMS algorithm. Results for 6 different interleaver schemes for EVR in the range 1-100 are presented.

Paper Details

Date Published: 17 April 2006
PDF: 15 pages
Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 62470Y (17 April 2006); doi: 10.1117/12.663456
Show Author Affiliations
Uwe Meyer-Baese, Florida State Univ. (United States)


Published in SPIE Proceedings Vol. 6247:
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV
Harold H. Szu, Editor(s)

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