自适应多模盲均衡的类牛顿算法

K. N. Paracha, A. Zerguine
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引用次数: 3

摘要

本工作集中于开发一种类似牛顿的盲均衡算法。该算法通过对最近提出的p阶软约束满足多模盲均衡算法(SCS-MMA-p)应用复泰勒级数得到。提出的算法是SCS-MMA-p的广义版本。该算法称为p阶随机类牛顿软约束满足多模算法(SNL-SCS-MMA-p)。在波特间隔和分数间隔均衡器上进行的仿真结果表明,在不同的参数p值下,所提算法的性能比SCS-MMA有很大的提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Newton-like algorithm for adaptive multi-modulus blind equalization
This work concentrates on the development of a Newton-like algorithm for blind equalization. This algorithm is obtained by applying a complex Taylor series on a recently proposed soft constraint satisfaction multi-modulus blind equalization algorithm of order p (SCS-MMA-p). The proposed algorithm is a generalized version of the SCS-MMA-p. This algorithm is called stochastic Newton-like soft constraint satisfaction multi-modulus algorithm of order p (SNL-SCS-MMA-p). Simulation results carried out, on both baud spaced and fractionally-spaced equalizers, to assess the performance of the prosed algorithm reveal great improvement in performance of the proposed algorithm against that of the SCS-MMA for different values of the parameter p.
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