Iterative Narrowband Interference Suppression for DS-CDMA Systems Using Feed-Forward Neural Network

This paper proposes a feed-forward neural network predictor to adaptively estimate and suppress the narrowband interference (NBI) in the Direct Sequence-Code Division Multiple Access (DS-CDMA) signal. The iterative code-aided estimation is used to further improve the system performance. Simulation results reveal that the proposed algorithm outperforms conventional linear prediction filtering and recurrent neural networks (RNN) based NBI rejection methods, in different interference models.