Detection Guided NLMS Estimation of Sparsely Parametrized Channels

chenpeng3361

Detection Guided NLMS Estimation of Sparsely Parametrized Channels

Abstract—We consider the normalized least mean square
(NLMS) estimation of a channel, which may be well approximated
by a finite impulse response model with sparsely separated
active or nonzero taps. Previously reported analyses imply that
the convergence rate of the NLMS estimator should be greatly
enhanced if only the active taps are estimated. We propose an
NLMS estimator, which incorporates a least squares based active
tap detection method. Simulations demonstrate that the NLMS
estimator has significantly faster convergence than the standard
NLMS estimator for colored as well as white input signals. Furthermore,
for sparse channels, this improved convergence speed is
accompanied by a lower computational cost.

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