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| Parallel pipelined least-mean-square algorithm and its performance analysis | |
| 作者姓名： | SHANG Yong WU Shunjun XIANG Haige |
| 作者单位： | 1.Department of Electronics' Peking University' Beijing 100871' China;2.Key Laboratory of Radar Signal Processing' Xidian University. Xi'an 710071. China,Key Laboratory of Radar Signal Processing' Xidian University. Xi'an 710071. China,Department of Electronics' Peking University' Beijing 100871' China |
| 摘 要： | A novel parallel pipelined least-mean-square algorithm is proposed by introducing parallel processing into the pipelined least-mean-square algorithm. The algorithm presented in this paper has smaller pipelined delay, higher data throughput rate and faster convergence speed, as well as wider step size range in which the convergence behavior of the algorithm is maintained than the pipelined least-mean-square algorithm. It also exhibits some de-correlation effect for the correlated input sequence. These properties make it more suitable for the cases of higher order filter with faster convergence speed. In addition, it can also be used to simplify the hardware implementation of filters. |
| 关 键 词： | parallel processing adaptive filtering parallel pipelined least-mean-square algorithm performance analysis. |
Parallel pipelined least-mean-square algorithm and its performance analysis |
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| SHANG Yong,WU Shunjun,XIANG Haige.Parallel pipelined least-mean-square algorithm and its performance analysis[J].Progress in Natural Science,2002,12(1):69-72. | |
| Authors: | SHANG Yong Wu Shunjun Xiang Haige |
| Institution: | 1. Department of Electronics, Peking University, Beijing 100871, China; Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China 2. Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China 3. Department of Electronics, Peking University, Beijing 100871, China |
| Abstract: | A novel parallel pipelined least-mean-square algorithm is proposed by introducing parallel processing into the pipelined least-mean-square algorithm. The algorithm presented in this paper has smaller pipelined delay, higher data throughput rate and faster convergence speed, as well as wider step size range in which the convergence behavior of the algorithm is maintained than the pipelined least-mean-square algorithm. It also exhibits some de-correlation effect for the correlated input sequence. These properties make it more suitable for the cases of higher order filter with faster convergence speed. In addition, it can also be used to simplify the hardware implementation of filters. |
| Keywords: | parallel processing adaptive filtering parallel pipelined least-mean-square algorithm performance analysis |
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