Adaptive Linear Filtering Design with Minimum Symbol Error Probability Criterion

作     者：Sheng Chen 

作者机构：School of Electronics and Computer Science University of Southampton Highfield Southampton SO17 1B J U.K. 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2006年第3卷第3期

页      码：291-303页

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.

主 题 词：Adaptive filtering mean square error probability density function non-Gaussian distribution Parzen window estimate symbol error rate stochastic gradient algorithm. 

学科分类：080902[080902] 0809[工学-计算机类] 08[工学] 

D　O　I：10.1007/s11633-006-0291-6

馆 藏 号：203433398...