Discrete-Time Wavelets

Doroslovački, Miloš

doi:10.1007/978-94-015-9715-9_1

Cited by 1 publication

(2 citation statements)

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“…In this case, the system identification can be easier and more efficient. Here, we concentrate on discrete-time linear systems modeled by discrete-time wavelets.1 A linear system can be represented by a weighting function x(t, 1). In this case, we have for the causal system output y(t) = x(t,l)u(l), (1) where u(t) is the input.…”

Section: Introductionmentioning

confidence: 99%

“…Here, we concentrate on discrete-time linear systems modeled by discrete-time wavelets.1 A linear system can be represented by a weighting function x(t, 1). In this case, we have for the causal system output y(t) = x(t,l)u(l), (1) where u(t) is the input. The weighting function x(t, 1) = 0, when 1 > t. To represent the same system we can use the function b(t, r) = x(t, t -'r).…”

Section: Introductionmentioning

confidence: 99%

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<title>Wavelet-based communication channel modeling and identification</title>

Doroslovački

Stanković

2002

Wavelet and Independent Component Analysis Applications IX

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The theory of modeling linear communication channels by wavelets is reviewed. It is shown that waveletbased channel identification can converge fast and be a flexible tool for dealing with many different signal propagation situations. Moreover, wavelet-based identification can provide robustness against narrow band noise and/or impulse noise. Possibilities for sparse channel representations are emphasized and illustrated by real life examples. A wavelet packet decomposition that maximizes the convergence speed of the LMS algorithm used in channel identification is discussed. Comparisons with the DFT, DCT, and other transforms are given.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%