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Fractionally-spaced constant modulus algorithm blind equalizer error surface characterization : effects of source distributions

机译:分数间隔恒模算法盲均衡器误差表面表征:源分布的影响

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摘要

The constant modulus algorithm (CMA) is a popular blind equalization algorithm. A common device used in demonstrating the convergence properties of CMA is the assumption that the source sequence is i.i.d. (independent, identically distributed). Previous results in the literature show that a finite length fractionally-spaced equalizer allows for perfect equalization of moving average channels (under certain channel conditions known as zero-forcing criteria). CMA has previously been shown to converge to such perfectly equalizing settings under an independent, platykurtic source. This paper investigates the effect of the distribution from which an independent source sequence is drawn on the CMA error surface and stationary points in the perfectly-equalizable fractionally-sampled equalizer case. Results include symbolic identification of all stationary points, as well as the eigenvalues and eigenvectors associated with their Hessian matrix. Results show quantitatively the loss of error surface curvature (in both direction and magnitude) at all stationary points. Simulations included demonstrate the affect this has on convergence speed.
机译:恒模算法(CMA)是一种流行的盲均衡算法。证明CMA收敛特性的常用设备是假设源序列为i.d. (独立,分布均匀)。文献中的先前结果表明,有限长度的分数间隔均衡器可以对移动平均信道进行完美均衡(在某些信道条件下称为零强制标准)。先前已证明CMA在独立的platykurtic信号源下收敛到如此完美的均衡设置。本文研究了在完全均衡的分数采样均衡器情况下,在CMA误差面上和固定点上绘制独立源序列的分布的影响。结果包括所有固定点的符号识别,以及与它们的Hessian矩阵相关的特征值和特征向量。结果定量显示了所有固定点的误差表面曲率(方向和大小)的损失。包括的仿真表明了这对收敛速度的影响。

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