Adaptive linear filtering design with minimum symbol error probability criterion

被引:0
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作者
Sheng Chen
机构
[1] University of Southampton,School of Electronics and Computer Science
关键词
Adaptive filtering; mean square error; probability density function; non-Gaussian distribution; Parzen window estimate; symbol error rate; stochastic gradient algorithm;
D O I
10.1007/s11633-006-0291-6
中图分类号
学科分类号
摘要
Adaptive digital filtering has traditionally been developed based on the minimum mean square error (MMSE) criterion and has found ever-increasing applications in communications. This paper presents an alternative adaptive filtering design based on the minimum symbol error rate (MSER) criterion for communication applications. It is shown that the MSER filtering is smarter, as it exploits the non-Gaussian distribution of filter output effectively. Consequently, it provides significant performance gain in terms of smaller symbol error over the MMSE approach. Adopting Parzen window or kernel density estimation for a probability density function, a block-data gradient adaptive MSER algorithm is derived. A stochastic gradient adaptive MSER algorithm, referred to as the least symbol error rate, is further developed for sample-by-sample adaptive implementation of the MSER filtering. Two applications, involving single-user channel equalization and beamforming assisted receiver, are included to demonstrate the effectiveness and generality of the proposed adaptive MSER filtering approach.
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页码:291 / 303
页数:12
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