Blind sequence-length estimation of low-SNR cyclostationary sequences

Abstract

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 paper presents two techniques to estimate the sequence length of a baseband DSSS signal affected by additive white Gaussian noise (AWGN). 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 (PCA). Theoretical analysis and computer simulation show that the second technique can correctly estimate the sequence length at a lower signal-to-noise ratio (SNR) than the first technique. The techniques presented in this paper can estimate the sequence length blindly which can then be fed to semi-blind detection and estimation algorithms.