| Robust Blind Adaptive Channel Equalization in Chaotic Communication Systems |
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| 作者姓名： | 张家树 |
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| 作者单位： | Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu 610031 |
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| 基金项目： | Supported by the National Natural Science Foundation of China under Grant No 60572027, the Programme for New Century Excellent Talents in University of China under Grant No NCET-05-0794, the National Key Lab of Anti-Jamming Communication Foundation of UESTC of China under Grant Nos 51434110104QT2201 and 51435080104QT2201. |
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| 摘 要： | Based on the bounded property and statistics of chaotic signal and the idea of set-membership identification, we propose a set-membership generalized least mean square （SM-GLMS） algorithm with variable step size for blind adaptive channel equalization in chaotic communication systems. The steady state performance of the proposed SM-GLMS algorithm is analysed, and comparison with an extended Kalman filter （EKF）-based adaptive algorithm and variable gain least mean square （VG-LMS） algorithm is performed for blind adaptive channel equalization. Simulations show that the proposed SM-GLMS algorithm can provide more significant steady state performance improvement than the EKF-based adaptive algorithm and VG-LMS algorithm.
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| 关 键 词： | 混沌通信系统 鲁棒性 自适应盲信道均衡 混沌信号 |
| 收稿时间： | 2006-08-18 |
| 修稿时间： | 2006-08-18 |
Robust Blind Adaptive Channel Equalization in Chaotic Communication Systems |
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| ZHANG Jia-Shu.Robust Blind Adaptive Channel Equalization in Chaotic Communication Systems[J].Chinese Physics Letters,2006,23(12):3187-3189. |
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| Authors: | ZHANG Jia-Shu |
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| Institution: | Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu 610031 |
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| Abstract: | Based on the bounded property and statistics of chaotic signal and the idea of set-membership identification, we propose a set-membership generalized least mean square (SM-GLMS) algorithm with variable step size for blind adaptive channel equalization in chaotic communication systems. The steady state performance of the proposed SM-GLMS algorithm is analysed, and comparison with an extended Kalman filter (EKF)-based adaptive algorithm and variable gain least mean square (VG-LMS) algorithm is performed for blind adaptive channel equalization. Simulations show that the proposed SM-GLMS algorithm can provide more significant steady state performance improvement than the EKF-based adaptive algorithm and VG-LMS algorithm. |
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| Keywords: | 05 45 -a |
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