Narrowband Interference Suppression Using RKF-Based Recurrent Neural Network in Spread Spectrum System

Abstract

A new adaptive neural network predictor to eradicate the narrowband interference in the spread spectrum system is proposed in this paper. The effectively robust Kalman filter (RKF) algorithm is adopted to adjust the synaptic weights in the nonlinear recurrent architecture and thereby estimate the narrowband interference. The main characteristics of the proposed RKF-based canceller are its rapid convergence rate and precise prediction. Simulation results reveal that the RNNP based on RKF algorithm has large improvement on the interference suppression capability compared with conventional LMS, ACM and RTRL-based canceller in CWI and ARI environments, respectively.