A Neural Network Method for Blind Signature Waveform Estimation of Synchronous CDMA Signals

Abstract

A principal component analysis (PCA) neural network (NN) based on signal eigen-analysis is proposed to blind signature waveform estimation in low signal to noise ratios (SNR) direct sequence synchronous code-division multiple-access (S-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, the signature waveforms can be estimated by the principal eigenvectors of autocorrelation matrix blindly. Additionally, the eigen-analysis method becomes inefficiency when the estimated vector becomes longer. In this case, we can use the PCA NN method to realize the blind signature waveform estimation from low SNR input signals effectively.