| Wavelet Neural Networks for Adaptive Equalization by Using the Orthogonal Least Square Algorithm |
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| 作者姓名： | 江铭虎 邓北星 GeorgesGielen |
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| 作者单位： | [1]LabofComputationalLinguistics,DepartmentofChineseLanguage,TsinghuaUniversity,Beijing100084,China [2]DepartmentofElectronicEngineering,TsinghuaUniversity,Beijing100084,China [3]DepartmentofElectricalEngineering,K.U.Leuven,KasteelparkArenberg10,B3001Heverlee,Belgium |
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| 基金项目： | the Tsinghua University Research Foundation，the Excellent Young Teacher Program of the Ministry of Education，and the Returnee Science Research Startup Fund of the Ministry of Education of China |
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| 摘 要： | Equalizers are widely used in digital communication systems for corrupted or time varying channels. To overcome performance decline for noisy and nonlinear channels, many kinds of neural network models have been used in nonlinear equalization. In this paper, we propose a new nonlinear channel equalization, which is structured by wavelet neural networks. The orthogonal least square algorithm is applied to update the weighting matrix of wavelet networks to form a more compact wavelet basis unit, thus obtaining good equalization performance. The experimental results show that performance of the proposed equalizer based on wavelet networks can significantly improve the neural modeling accuracy and outperform conventional neural network equalization in signal to noise ratio and channel non-linearity.
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| 关 键 词： | 小波神经网络 正交最小二乘法 WNNs OLS 自适应均衡 信道均衡 |
Wavelet Neural Networks for Adaptive Equalization by Using the Orthogonal Least Square Algorithm |
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| JIANG Minghu DENG Beixing ,Georges Gielen . Lab of Computational Linguistics.Wavelet Neural Networks for Adaptive Equalization by Using the Orthogonal Least Square Algorithm[J].Tsinghua Science and Technology,2004,9(1):24-29,37. |
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| Authors: | JIANG Minghu DENG Beixing Georges Gielen Lab of Computational Linguistics |
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| Institution: | JIANG Minghu DENG Beixing 2,Georges Gielen3 1. Lab of Computational Linguistics,Department of Chinese Language,Tsinghua University,Beijing 100084,China, 2. Department of Electronic Engineering,Tsinghua University,Beijing 100084,China, 3. Department of Electrical Engineering,K.U.Leuven,Kasteelpark Arenberg 10,B3001 Heverlee,Belgium |
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| Abstract: | Equalizers are widely used in digital communication systems for corrupted or time varying channels. To overcome performance decline for noisy and nonlinear channels, many kinds of neural network models have been used in nonlinear equalization. In this paper, we propose a new nonlinear channel equalization, which is structured by wavelet neural networks. The orthogonal least square algorithm is applied to update the weighting matrix of wavelet networks to form a more compact wavelet basis unit, thus obtaining good equalization performance. The experimental results show that performance of the proposed equalizer based on wavelet networks can significantly improve the neural modeling accuracy and outperform conventional neural network equalization in signal to noise ratio and channel non-linearity. |
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| Keywords: | adaptive equalization wavelet neural networks (WNNs) orthogonal least square (OLS) |
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