| An ICA and EC based
approach for blind equalization and channel parameter estimation |
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| 作者姓名： | YANG Lüxi |
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| 作者单位： | HE Zhenya,YANG Lüxi,WEI Chengjian(Department of Radio Engineering, Southeast University, Nanjing 210096, China);LIU Ju(Department of Radio Engineering, Southeast University, Nanjing 210096, China;Department of Electronic Engineering, Shandong University, Jinan 250100, China)
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| 摘 要： | A new on-line blind equalization approach is proposed. The approach combines over-sampling technique with independent component analysis (ICA) neural network and can give equalized output on-line employing only the received signal. Based on the fourth-order cumulants and the characteristic of the linear system, the parameters of original channel are also estimated using evolutionary computation (EC). Compared to traditional equalization methods, the proposed algorithm is of simple architecture, does not need learning sequences apart from the observation, and can achieve both blind equalization and system identification. Computer simulations show good performance.
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Basic research in the field of thermal infrared remote sensing |
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| Guanhua?Xu.An ICA and EC based
approach for blind equalization and channel parameter estimation[J].Science in China(Technological Sciences),2000,43(1):1-8. |
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| Authors: | Guanhua Xu |
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| Affiliation: | 1. Department of Radio Engineering, Southeast University, Nanjing 210096, China
2. Department of Radio Engineering, Southeast University, Nanjing 210096, China;Department of Electronic Engineering, Shandong University, Jinan 250100, China |
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| Abstract: | A new on-line blind equalization approach is proposed. The approach combines over-sampling technique with independent component analysis (ICA) neural network and can give equalized output on-line employing only the received signal. Based on the fourth-order cumulants and the characteristic of the linear system, the parameters of original channel are also estimated using evolutionary computation (EC). Compared to traditional equalization methods, the proposed algorithm is of simple architecture, does not need learning sequences apart from the observation, and can achieve both blind equalization and system identification. Computer simulations show good performance. |
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| Keywords: | independent component analysis higher-order cumulants evolutionary computation blind equal-ization |
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