Journal of Signal and Information Processing 7, pp. 227-251.
ISSN/ISBN: Not available at this time. DOI: 10.4236/jsip.2016.74020
Abstract: In the communication field, during transmission, a source signal undergoes a convolutive distortion between its symbols and the channel impulse response. This distortion is referred to as Intersymbol Interference (ISI) and can be reduced significantly by applying a blind adaptive deconvolution process (blind adaptive equalizer) on the distorted received symbols. But, since the entire blind deconvolution process is carried out with no training symbols and the channel’s coefficients are obviously unknown to the receiver, no actual indication can be given (via the mean square error (MSE) or ISI expression) during the deconvolution process whether the blind adaptive equalizer succeeded to remove the heavy ISI from the transmitted symbols or not. Up to now, the output of a convolution and deconvolution process was mainly investigated from the ISI point of view. In this paper, the output of a convolution and deconvolution process is inspected from the leading digit point of view. Simulation results indicate that for the 4PAM (Pulse Amplitude Modulation) and 16QAM (Quadrature Amplitude Modulation) input case, the number “1” is the leading digit at the output of a convolution and deconvolution process respectively as long as heavy ISI exists. However, this leading digit does not follow exactly Benford’s Law but follows approximately the leading digit (digit 1) of a Gaussian process for independent identically distributed input symbols and a channel with many coefficients.
Bibtex:
@article {,
AUTHOR = {Pinchas, Monika},
TITLE = {Inspection of the Output of a Convolution and Deconvolution Process from the Leading Digit Point of View—Benford’s Law},
JOURNAL = {Journal of Signal and Information Processing},
YEAR = {2016},
VOLUME = {7},
PAGES = {227--251},
DOI = {10.4236/jsip.2016.74020},
URL = {http://www.scirp.org/(S(lz5mqp453edsnp55rrgjct55))/journal/PaperInformation.aspx?PaperID=72166},
Reference Type: Journal Article
Subject Area(s): Computer Science, Probability Theory, Statistics