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Convergence and Performance Analysis of Godard Family and Multimodulus Algorithms for Blind Equalization

机译:盲均衡的Godard族和多模算法的收敛性和性能分析

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We obtain the convergence of the Godard family [including the Sato and constant modulus (CM) algorithms] and the multimodulus algorithms (MMA) in a unified way. Our analysis also works for CMA fractionally spaced equalizer (FSE). Our assumptions are quite realistic: The channel input can be asymptotically stationary and ergodic, the channel impulse response is finite and can be stationary and ergodic (this models fading channels), and the equalizer length is finite. The noise is independent and identically distributed (i.i.d.). The channel input can be discrete or continuous. Our approach allows us to approximate the whole trajectory of the equalizer coefficients. This provides estimates of the rate of convergence, and the system performance (symbol error rate) can be evaluated under transience and steady state.
机译:我们以统一的方式获得了Godard系列[包括Sato和常数模(CM)算法]和多模算法(MMA)的收敛。我们的分析还适用于CMA分数间隔均衡器(FSE)。我们的假设是很现实的:通道输入可以是渐近平稳的和遍历的,通道脉冲响应是有限的,并且可以是平稳的和遍历的(此模型对衰落的通道进行建模),并且均衡器的长度是有限的。噪声是独立的并且分布均匀(i.d.)。通道输入可以是离散的或连续的。我们的方法使我们能够近似均衡器系数的整个轨迹。这提供了收敛速度的估计,并且可以在瞬态和稳态下评估系统性能(符号错误率)。

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