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

Tianqi ZHANG; Chao ZHANG

doi:10.1093/ietcom/e89-b.6.1943

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

Tianqi ZHANG, Chao ZHANG

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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.

Publication: IEICE TRANSACTIONS on Communications Vol.E89-B No.6 pp.1943-1946

Publication Date: 2006/06/01

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Online ISSN: 1745-1345

DOI: 10.1093/ietcom/e89-b.6.1943

Type of Manuscript: LETTER

Category: Wireless Communication Technologies

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Tianqi ZHANG, Chao ZHANG, "An Unsupervised Adaptive Method to Eigenstructure Analysis of Lower SNR DS Signals" in IEICE TRANSACTIONS on Communications, vol. E89-B, no. 6, pp. 1943-1946, June 2006, doi: 10.1093/ietcom/e89-b.6.1943.
Abstract: 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.
URL: https://globals.ieice.org/en_transactions/communications/10.1093/ietcom/e89-b.6.1943/_p

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@ARTICLE{e89-b_6_1943,
author={Tianqi ZHANG, Chao ZHANG, },
journal={IEICE TRANSACTIONS on Communications},
title={An Unsupervised Adaptive Method to Eigenstructure Analysis of Lower SNR DS Signals},
year={2006},
volume={E89-B},
number={6},
pages={1943-1946},
abstract={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.},
keywords={},
doi={10.1093/ietcom/e89-b.6.1943},
ISSN={1745-1345},
month={June},}

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TY - JOUR
TI - An Unsupervised Adaptive Method to Eigenstructure Analysis of Lower SNR DS Signals
T2 - IEICE TRANSACTIONS on Communications
SP - 1943
EP - 1946
AU - Tianqi ZHANG
AU - Chao ZHANG
PY - 2006
DO - 10.1093/ietcom/e89-b.6.1943
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E89-B
IS - 6
JA - IEICE TRANSACTIONS on Communications
Y1 - June 2006
AB - 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.
ER -