An Accelerated Constant Modulus Algorithm

Magno T. M. Silva, M. Gerken, M. D. Miranda
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引用次数: 1

Abstract

— We present a novel adaptive algorithm for blind equalization. It is based on a tuner used in adaptive control that sets the second derivative of the parameter estimates and minimizes the cost function introduced by Go- dard. Based on simulation results we present a comparison with the Constant Modulus and the Shalvi-Weinstein algorithms. Both the convergence speed and the computational complexity of the proposed algorithm lie between those of the Constant Modulus and the Shalvi-Weinstein algorithms, thus present- ing a more favorable compromise between convergence speed and computational complexity. Some preliminary results also suggest that the proposed al- gorithm presents a more robust behavior with respect to convergence to global minima.
一种加速恒模算法
提出了一种新的自适应盲均衡算法。它基于自适应控制中使用的调谐器,该调谐器设置参数估计的二阶导数并最小化Go- ard引入的代价函数。基于仿真结果,我们给出了与常模和Shalvi-Weinstein算法的比较。该算法的收敛速度和计算复杂度均介于常模算法和Shalvi-Weinstein算法之间,从而在收敛速度和计算复杂度之间呈现出更有利的折衷。一些初步结果还表明,该算法在收敛到全局极小值方面具有更强的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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