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. The attractive features of DS-CDMA system include its efficient utilization of channel bandwidth, the relative insensitivity to multipath, noise and interference. In spite of this robustness, the performance of CDMA system can be degraded by strong narrowband interferences.