Frequency-domain adaptive filtering - A set-membership approach

被引:0
|
作者
Guo, L [1 ]
Ekpenyong, A [1 ]
Huang, YF [1 ]
机构
[1] Univ Notre Dame, Dept Elect Engn, Notre Dame, IN 46556 USA
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Frequency-domain adaptive filtering has been shown to have the advantage of low complexity compared to its time-domain counterpart. In this paper, a frequency-domain Set-Membership Normalized Least-Mean-Square (F-SM-NLMS) algorithm is derived, and shown to have better performance (in terms of convergence and bit error rate (BER)) and less updates than frequency-domain Normalized Least-Mean-Square (F-NLMS) algorithm. We also propose an updator-sharing scheme to reduce filter complexity of frequency-domain adaptive filtering. The results show significant savings in complexity with little compromise of performance.
引用
收藏
页码:2073 / 2077
页数:5
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