Narrow-band interference suppression in spread-spectrum CDMA communications using pipelined recurrent neural networks

This paper investigates the application of pipelined recurrent neural netwo rks (PRNN's) to the narrow-band interference (NBI) suppression over spread- spectrum (SS) code-division multiple-access (CDMA) channels in the presence of additive white Gaussian noise (AWGN) plus non-Gaussian observation nois e. Optimal detectors and receivers for such channels are no longer linear. A PRNN that consists of a number of simpler small-scale recurrent neural ne twork (RNN) modules,vith less computational complexity is conducted to intr oduce best nonlinear approximation capability into the minimum mean-squared error nonlinear predictor model in order to accurately predict the NBI sig nal based on adaptive learning for each module from previous non-Gaussian o bservations, Once the prediction of the NBI signal is obtained, a resulting signal is computed by subtracting the estimate from the received signal, T hus, 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 ef ficiency. Simulation results show that PRNN-based NBI rejection provides a superior signal-to-noise ratio (SNR) improvement relative to the convention al adaptive nonlinear approximate conditional mean (ACM) filters, especiall y when the channel statistics and exact number of CDMA users are not known to those receivers.