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

Abstract

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.