An Unsupervised Adaptive Method to Eigenstructure Analysis of Lower SNR DS Signals

Tianqi ZHANG
Chao ZHANG

Publication
IEICE TRANSACTIONS on Communications   Vol.E89-B    No.6    pp.1943-1946
Publication Date: 2006/06/01
Online ISSN: 1745-1345
DOI: 10.1093/ietcom/e89-b.6.1943
Print ISSN: 0916-8516
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 eigen-analysis 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 non-overlapping 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 eigen-analysis 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 DS-SS signals effectively.