A neural network approach to blind-estimation of PN spreading sequence in lower SNR DS/SS signals

In this paper, an approach based on discrete Karhunen-Loeve transformation of the DS/SS signals is proposed to estimate PN sequence in lower SNR DS/SS signals. Characteristics of self-organization and principal components extraction of unsupervised neural networks are exploited adequately. Theoretical analysis and experimental results are provided to show that this approach can work well on the lower SNR input signals.