Learning characteristics of transpose-form LMS adaptive filters

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