Open AccessBlind sequence‐length estimation of low‐SNR cyclostationary sequences
Author(s) -
Vlok Jacobus David,
Olivier Jan Corné
Publication year - 2014
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2013.0616
Subject(s) - cyclostationary process , sequence (biology) , computer science , algorithm , estimation , statistics , speech recognition , pattern recognition (psychology) , mathematics , computational biology , artificial intelligence , telecommunications , biology , genetics , channel (broadcasting) , management , economics
Several existing direct‐sequence spread spectrum (DSSS) detection and estimation algorithms assume prior knowledge of the symbol period or sequence length, although very few sequence‐length estimation techniques are available in the literature. This study presents two techniques to estimate the sequence length of a baseband DSSS signal affected by additive white Gaussian noise. The first technique is based on a known autocorrelation technique which is used as reference, and the second technique is based on principal component analysis. Theoretical analysis and computer simulation show that the second technique can correctly estimate the sequence length at a lower signal‐to‐noise ratio than the first technique. The techniques presented in this study can estimate the sequence length blindly which can then be fed to semi‐blind detection and estimation algorithms.