Adapted wavelet analysis from theory to software
Adapted wavelet analysis from theory to software
The what, how, and why of wavelet shrinkage denoising
Computing in Science and Engineering
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
De-noising by soft-thresholding
IEEE Transactions on Information Theory
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This work presents an alternative processing scheme to improve measurement-based power delay profiles (PDP) estimates, using wavelet-based denoising. The usual PDP processing comprises cutting off all the data below a previously determined flat noise threshold. The proposed method is a more refined approach, since it tries to extract the noise from the measured data, keeping only the estimated signal. Wavelet denoising can be performed in many different ways, for it depends on several parameters, like the noise threshold selection rule, the wavelet function, the choice or not for a wavelet coefficients shrinkage and the number of decomposition levels. Several parameter combinations have been tested, in special for the Visu selection rule, which presented the best performance for the available data in the overall. Denoising was applied to real data from indoor environments, collected from wideband channel sounding surveys, centered at 1.8 GHz. Since frequency domain sounding has been carried out, denoising has been tested both directly over the frequency domain, and over the time--delay domain (PDP). The major result of the proposed processing was a qualitative improvement of the PDP, with smoother noise floors, and also with increases up to 8 dB on signal-to-noise ratios, in special for delay domain denoising.