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An Unsupervised Adaptive Method to Eigenstructure Analysis of Lower SNR DS Signals
Tianqi ZHANG Chao ZHANG
Publication
IEICE TRANSACTIONS on Communications
Vol.E89B
No.6
pp.19431946 Publication Date: 2006/06/01 Online ISSN: 17451345
DOI: 10.1093/ietcom/e89b.6.1943 Print ISSN: 09168516 Type of Manuscript: LETTER Category: Wireless Communication Technologies Keyword: principal component analysis (PCA), neural network (NN), pseudo noise (PN) sequence, direct sequence spread spectrum (DS),
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Summary:
An unsupervised adaptive signal processing method of principal components analysis (PCA) neural networks (NN) based on signal eigenanalysis is proposed to permit the eigenstructure analysis of lower signal to noise ratios (SNR) direct sequence spread spectrum (DS) signals. The objective of eigenstructure analysis is to estimate the pseudo noise (PN) of DS signals blindly. The received signal is firstly sampled and divided into nonoverlapping signal vectors according to a temporal window, which duration is two periods of PN sequence. Then an autocorrelation matrix is computed and accumulated by these signal vectors one by one. Lastly, the PN sequence can be estimated by the principal eigenvector of autocorrelation matrix. Since the duration of temporal window is two periods of PN sequence, the PN sequence can be reconstructed by the first principal eigenvector only. Additionally, the eigenanalysis method becomes inefficient when the estimated PN sequence is long. We can use an unsupervised adaptive method of PCA NN to realize the PN sequence estimation from lower SNR input DSSS signals effectively.

