Title :
Narrowband Interference Suppression Using RKF-Based Recurrent Neural Network in Spread Spectrum System
Author :
XU, Ding-jie ; ZHAO, Pi-jie ; Shen, Feng ; ZHAO, Hong
Author_Institution :
Autom. Coll., Harbin Eng. Univ., Harbin
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.
Keywords :
Kalman filters; interference suppression; recurrent neural nets; spread spectrum communication; telecommunication computing; Kalman filter algorithm; RKF-based recurrent neural network; narrowband interference suppression; neural network predictor; nonlinear recurrent architecture; spread spectrum system; synaptic weight; Adaptive systems; Convergence; Interference cancellation; Interference suppression; Least squares approximation; Narrowband; Neural networks; Recurrent neural networks; Robustness; Spread spectrum communication;
Conference_Titel :
Wireless Communications, Networking and Mobile Computing, 2008. WiCOM '08. 4th International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4244-2107-7
Electronic_ISBN :
978-1-4244-2108-4
DOI :
10.1109/WiCom.2008.433
