Interference Suppression Based on Wavelet Packets Transform and BP Neural Network for DSSS System

SHI Yangchun; WU Longsheng; LIU Youbao

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Interference Suppression Based on Wavelet Packets Transform and BP Neural Network for DSSS System

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- Interference Suppression Based on Wavelet Packets Transform and BP Neural Network for DSSS System
- Acta Scientiarum Naturalium Universitatis SunYatseni Vol. 50, Issue 5, Pages: 15-20(2011)
- 作者机构：
  
  西安微电子技术研究所,陕西,西安,710054
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2011，
  
  Published Online：25 September 2011，
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SHI Yangchun, WU Longsheng, LIU Youbao. Interference Suppression Based on Wavelet Packets Transform and BP Neural Network for DSSS System. [J]. Acta Scientiarum Naturalium Universitatis SunYatseni 50(5):15-20(2011)
DOI：

SHI Yangchun, WU Longsheng, LIU Youbao. Interference Suppression Based on Wavelet Packets Transform and BP Neural Network for DSSS System. [J]. Acta Scientiarum Naturalium Universitatis SunYatseni 50(5):15-20(2011) DOI：

摘要

提出功率分布优势小波包变换(PDP-WPT)和扩展BP神经网络(EBPNN)，并基于两者提出针对直扩系统(DSSS)的变换域信息信号提取(TISI)干扰抑制算法。首先采用PDP-WPT高效跟踪直扩系统中的敌意干扰，提高算法收敛速度；然后通过EBPNN对变换系数进行信息信号的自适应识别达到干扰抑制的目的，具有复杂度低、鲁棒性好的特点。理论分析得到采用TISI后的扰信比(ISR)抑制量、信噪比(SNR)损失量和误码率(BER)的数学表达式。仿真结果表明：在相同干扰信号的情况下，与两种传统算法相比较，本算法的扰信比抑制量分别提高了43.8%和20.8%，信噪比损失量分别降低了62.5%和34.8%。

Abstract

To suppress the interference in a DSSS system

a transform domain information signal identify (TISI) algorithm is proposed，based on two improved algorithms: Power distributing predominance wavelet packets transform (PDP-WPT) and extend BP neural network (EBPNN). Firstly

PDPWPT is proposed to track the interference signal effectively

improving the convergence rate of this algorithm. Secondly

the information signal can be identified from transform domain coefficients by self-adaptive EBPNN

which has simple structure and enhanced numerical robustness. Based on the math model of the TISL

the formulas for ISR suppression

SNR loss and BER are deduced. Results show that TISI can improve the ISR suppression by 43.8% and 20.8%

reduce the SNR loss by 62.5% and 348% separately compared with traditional algorithms in the condition of same interference input.

关键词

小波包变换BP神经网络直扩系统干扰抑制

Keywords

wavelet packets transformBP neural networkDSSSinterference suppression

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