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

被引：0

作者：

Xu, Ding-jie ^{[1
]}

Zhao, Pi-jie ^{[1
]}

Shen, Feng ^{[1
]}

Zhao, Hong ^{[1
]}

机构：

[1] Harbin Engn Univ, Automat Coll, Harbin, Peoples R China

来源：

2008 4TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING, VOLS 1-31 | 2008年

关键词：

spread spectrum system; narrowband interference; recurrent neural network; robust Kalman filter;

D O I：

暂无

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

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.

引用

页码：1819 / 1823

页数：5

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