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Adaptive blind equalization technique to enhance the Constant Modulus Algorithm performance

机译:自适应盲均衡技术,提升恒定模量算法性能

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Recently blind equalizers have a wide range of research interest since they do not require training sequence and extra bandwidth, but the main weaknesses of these approaches are their high computational complexity and slow adaptation, so different algorithms are presented to avoid this nature. This paper introduces a new blind equalization technique, the Exponentially Weighted Step-size Recursive Least Squares Constant Modulus Algorithm (EXP-RLS-CMA), based upon the combination between the Exponentially Weighted Step-size Recursive Least Squares (EXP-RLS) algorithm and the Constant Modulus Algorithm (CMA), by providing several assumptions to obtain faster convergence rate to an optimal delay where the Mean Squared Error (MSE) is minimum, and so this selected algorithm can be implemented in digital system to improve the receiver performance. Simulations are presented to show the excellence of this technique, and the main parameters of concern to evaluate the performance are, the rate of convergence, the mean square error (MSE), and the average error versus different signal-to-noise ratios.
机译:最近,盲均衡器具有广泛的研究兴趣,因为它们不需要培训序列和额外的带宽,但这些方法的主要弱点是它们的高计算复杂性和缓慢的适应,因此提出了不同的算法以避免这种性质。本文介绍了一种新的盲均衡技术,基于指数加权步长递归最小二乘(EXP-RLS)算法和eMP-SizeS算法之间的组合,所述盲均衡技术,指数加权分部递归最小值最小二乘恒定模数算法(EXP-RLS-CMA)常量模数算法(CMA)通过提供若干假设以获得更快的会聚速率,以获得平均平方误差(MSE)最小的最佳延迟,因此可以在数字系统中实现该所选算法以改善接收器性能。提出了仿真以展示这种技术的卓越,以及评估性能的关注的主要参数是,收敛速度,平均方误差(MSE),以及平均误差与不同的信噪比。

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