A New Variable Tap-Length LMS Algorithm to Model an Exponential Decay Impulse Response

Steady-State Performance Analysis of a Variable Tap-Length LMS Algorithm

“…We therefore conclude that L(1) L opt + 1, so that by also exploiting assumption A.1, the condition that Lopt L(1) Lopt +1 will always be used in the following derivations. In a manner similar to [3] and [5], in order to speed up the convergence rate of the FT variable tap-length LMS algorithm, the step size is made variable rather than fixed, according to the range of described in [3]: …”

“…Generally, values between 0.001 and 0.01 are good choices for . 5. The parameter is the step-size parameter that controls the adaptation process of the variable tap length.…”

“…Among existing variable tap-length LMS algorithms, some are designed to not only establish a suitable steady-state tap length, but also to speed up the convergence rate [3], [5]. These methods are based on the assumption that the unknown optimal filter has an impulse response sequence with an exponentially decaying envelope, which limits their utility.…”

Variable tap-length adaptive algorithm which exploits both second and fourth order statistics

“…Similar to that in [10] the tap-length L(n + 1) which will be used in the adaptation of the filter weights in the LMS algorithm is obtained from the fractional tap-length l f (n) according to (5), and the step size is chosen according to (6), followed by the update of the adaptive filter coefficients according to the LMS algorithm.…”

Adaptive fir filters with automatic length optimization by monitoring a normalized combination scheme

“…Although many analyses are still carried out under the assumption that the adaptive filter and the unknown system are matched in size, several authors have also investigated algorithms for realizing and controlling filters that use a variable number of filter taps [2,3]. However, these methods often still rely on some kind of a-priori assumption [4] or have not yet been investigated for systems with very high filter order [5] as present in many practical applications. In this paper, we propose a novel method for the automatic self-configuration of adaptive filters in terms of memory size by exploiting the inherent soft decision property of the adaptive mixing of a normalized filter combination.…”