NDLI: A Neural Network Method to Adaptive Feature Extraction of Weak DS-CDMA Signals

Content Provider	IEEE Xplore Digital Library
Author	Zhang, Tianqi Dai, Shaosheng Li, Xuesong Yang, Liufei
Copyright Year	2008
Abstract	This paper introduces an on-line unsupervised LEArning neural network (NN) for adaptive feature extraction via Principal component analysis (LEAP) of lower signal to noise ratios (SNR) direct sequence code-division multiple-access (DS-CDMA) signals. The proposed method is based on eigen-analysis of DS-CDMA signals, and exploits the cyclo-stationarity of communication signals adequately. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, which duration is a period of pseudo noise (PN) sequence (signature waveform). Then an autocorrelation matrix is computed and accumulated by these signal vectors one by one. Since the synchronous point between the symbol waveform and observation window is a randomize determination point, therefore, each vector must contain all information of a whole period of PN sequence. In the end, the PN sequence and its strength can be extracted by the principal eigenvectors and their associated eigenvalues of autocorrelation matrix blindly. But, the eigen-analysis method is belongs to a batch method, it is difficult to real-time implementation. We can use the LEAP NN method to realize on-line adaptive principal feature extraction and tracking of the weak input DS-CDMA signals effectively.
Starting Page	385
Ending Page	389
File Size	429562
Page Count	5
File Format	PDF
ISBN	9780769531199
DOI	10.1109/CISP.2008.140
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive systems Neural networks Feature extraction Eigenvalues and eigenfunctions Multiaccess communication Autocorrelation Data mining Unsupervised learning Signal to noise ratio Principal component analysis
Content Type	Text
Resource Type	Article

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