Analysis of the multiple-error and block least-mean-square adaptive algorithms
Analysis of the multiple-error and block least-mean-square adaptive algorithms
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DOI：
10.1109/82.365348
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发表时间：
1995-02
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通讯作者：
S. Douglas
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文献类型：
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作者：
S. Douglas
在一些基于块的和频域的滤波任务中以及在多信道滤波应用中，采用了由W/sub k+1/=W/sub k/+/spl mu/X/sub k/（D/sub k/-X/sub k//sup T/W/sub k/）给出的多误差LMS自适应算法。输入数据通道。我们对此算法提供了一种新的均方分析，该分析解释了数据矩阵X/sub k/中连续数据向量之间的相关性。使用我们的分析，我们表明，相关和独立同分布。输入数据通道，对于给定的数据消耗量，多误差LMS算法的性能一致地比单通道LMS算法差。我们还推导出简单的步长范围，以保证我们的相关数据模型的多误差和块LMS自适应算法的均方收敛。块LMS自适应算法和多通道滤波-X LMS自适应算法的仿真验证了我们的理论结果。>
In some block-based and frequency-domain filtering tasks and in multichannel filtering applications, a multiple-error LMS adaptive algorithm, given by W/sub k+1/=W/sub k/+/spl mu/X/sub k/(D/sub k/-X/sub k//sup T/W/sub k/), is employed, In this paper, we examine the mean-square performance of the multiple-error LMS adaptive algorithm for correlated Gaussian input data channels and arbitrary i.i.d. input data channels. We provide a new mean-square analysis of this algorithm that accounts for the correlations between successive data vectors in the data matrix X/sub k/. Using our analysis, we show that for both correlated and i.i.d. input data channels, the multiple-error LMS algorithm performs uniformly worse than the single-channel LMS algorithm for a given amount of data consumed. We also derive simple step size bounds to guarantee mean-square convergence of the multiple-error and block LMS adaptive algorithms for our correlated data model. Simulations of both the block LMS adaptive algorithm and the multichannel filtered-X LMS adaptive algorithm corroborate our theoretical results. >