Noise-constrained least mean squares algorithm

We consider the design of an adaptive algorithm for finite impulse response channel estimation, which incorporates partial knowledge of the channel, specifically, the additive noise variance. Although the noise variance is not required for the offline Wiener solution, there are potential benefits (a...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2001-09, Vol.49 (9), p.1961-1970
Main Authors: Yongbin Wei, Gelfand, S.B., Krogmeier, J.V.
Format: Article
Language:English
Subjects:
Adaptive algorithm
Adaptive algorithms
Additive noise
Additive white noise
Algorithm design and analysis
Algorithms
Applied sciences
AWGN
Channel estimation
Channels
Computer simulation
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Gaussian noise
Impulse response
Information, signal and communications theory
Least mean squares algorithm
Least squares approximation
Mean square error methods
Noise
Signal and communications theory
Signal processing algorithms
Signal, noise
Studies
Telecommunications and information theory
Variance
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Summary:We consider the design of an adaptive algorithm for finite impulse response channel estimation, which incorporates partial knowledge of the channel, specifically, the additive noise variance. Although the noise variance is not required for the offline Wiener solution, there are potential benefits (and limitations) for the learning behavior of an adaptive solution. In our approach, a Robbins-Monro algorithm is used to minimize the conventional mean square error criterion subject to a noise variance constraint and a penalty term necessary to guarantee uniqueness of the combined weight/multiplier solution. The resulting noise-constrained LMS (NCLMS) algorithm is a type of variable step-size LMS algorithm where the step-size rule arises naturally from the constraints. A convergence and performance analysis is carried out, and extensive simulations are conducted that compare NCLMS with several adaptive algorithms. This work also provides an appropriate framework for the derivation and analysis of other adaptive algorithms that incorporate partial knowledge of the channel.
ISSN:1053-587X
1941-0476
DOI:10.1109/78.942625