Narrowband interference suppression in spread spectrum CDMA communications using pipelined recurrent neural networks

This paper investigates the application of pipelined recurrent neural networks (PRNN) to the narrowband interference (NBI) suppression over spread spectrum CDMA channels in the presence of AWGN plus non-Gaussian observation noise. Optimal detectors and receivers for such channels are no longer linear. A PRNN that consists of a number of simpler small-scale recurrent neural network (RNN) modules with less computational complexity is conducted to introduce the best nonlinear approximation capability into the minimum mean squared error nonlinear predictor model in order to accurately predict the NBI signal based on adaptive learning for each module from previous non-Gaussian observations. Once the prediction of the NBI signal is obtained, a resulting signal is computed by subtracting the estimate from the received signal. Thus, the effect of the NBI can be reduced. Moreover, since those modules of a PRNN can be performed simultaneously in a pipelined parallelism fashion, this would lead to a significant improvement in its total computational efficiency. Simulation results show that PRNN-based NBI rejection provides a superior SNR improvement relative to the conventional adaptive nonlinear ACM filters, especially when the channel statistics and the exact number of CDMA users are not known to those receivers