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

fYear

2008

fDate

12-14 Oct. 2008

Firstpage

1

Lastpage

5

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/WiCom.2008.433

Filename

4678342