Adaptive Feature Extraction of Lower SNR DS-CDMA signals

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. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, which duration is a period of signature waveform. Then an autocorrelation matrix is computed and accumulated by these signal vectors one by one. Since we have assumed that the synchronous point between the symbol waveform and observation window have been known, the signal vectors may be sampled and divided at the beginning of this synchronous point, therefore, each vector must contain all information of signature waveforms. In the end, each signature waveform and its strength can be extracted by the principal eigenvector and its associated eigenvalue of autocorrelation matrix blindly. However, the eigenanalysis method belongs to a batch processing method, it becomes inefficiency when it uses in real-time signal process and input signal tracking. In this case, we can use the LEAP NN method to realize on-line adaptive principal feature extraction of the lower SNR input DS-CDMA signals effectively