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Learning characteristics of transpose-form LMS adaptive filters

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1 Author(s)
Jones, D.L. ; Coordinated Sci. Lab., Illinois Univ., Urbana, IL, USA

Transpose-form filter structures have several advantages over direct-form structures for high-speed, parallel implementation of finite impulse response (FIR) filters. Transpose-form least mean square (LMS) adaptive filter architectures are often used in parallel implementations; however, the behavior of these filters differs from the standard LMS algorithm and has not been adequately studied. A method for determining the maximum convergence factor yielding convergence of the mean of the transpose-form LMS adaptive filter taps is developed. The analysis reveals the great similarity of transpose-form LMS adaptive filters to delayed-update LMS adaptive filters, which have been much more fully characterized

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Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on  (Volume:39 ,  Issue: 10 )