Abstract:

Aiming at the problem of the batch processing method with high complexity and slow convergence speed for the pseudonoise (PN) code sequence blind estimate of synchronous direct sequence code-division multiple access (DS-CDMA) signals under different power level, three multi-principal component neural networks (NNs) are introduced—Sanger NN, LEAP NN and APEX NN. Firstly, the period segmented DSCDMA signals are chosen as the neural network input and the symbol function of each weight vector is used to represent the PN code sequence of each user. Then through the continuous input signal, the weight vectors of the NN are trained repeatedly until convergence. Finally, the PN code sequence of each user can be rebuilt by the symbolic function of each weight vector. Furthermore, an optimal variable step convergence model is put forward via the recursive least square (RLS), which improves the convergence speed of the network greatly. Theory analysis and simulation results show that the complexity of three kinds of NNs when used to the PN code sequence blind estimate of synchronous DS-CDMA signals under different power level is reduced significantly, and when the signal to noise ratio (SNR) is -20 dB and the number of users is 10, the PN code sequence of synchronous DS-CDMA signals under different power level can still be estimated by LEAP NN and Sanger NN efficiently. Compared with LEAP NN and Sanger NN, APEX NN is poor relatively. In addition, LEAP NN consumes lager memory but it has fast convergence speed. APEX NN is contrary to LEAP NN; Sanger NN is between the LEAP NN and the APEX NN.