A Proposal of Normalized Adaptive Direct Blind Equalization and its application

Minoru Komatsu, N. Tanabe
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Abstract

This paper proposes normalized adaptive direct blind equalization and its application. There is adaptive direct blind equalization in noisy environments as one of conventional methods. It is known that the conventional method is higher precision even when the observed signals are included the noises. However, the conventional method has the problem that there is lower convergence rate because this method is adaptive algorithm based on Least Square method. In this paper, for solving this problem, we describe normalized adaptive direct blind equalization and propose the method which uses both proposed method and conventional method. The features of proposed method are (i) realization of high performance and stable equalizer estimation with Data Least square(DLS) [1] and (ii) realization of high convergence rate and recovery performance. We show the effectiveness of the proposed method using computer simulations.
一种归一化自适应直接盲均衡方法及其应用
提出了归一化自适应直接盲均衡及其应用。噪声环境下的自适应直接盲均衡是传统的盲均衡方法之一。传统的方法在考虑噪声的情况下仍具有较高的精度。但传统方法由于是基于最小二乘法的自适应算法,存在收敛速度较慢的问题。为了解决这一问题,本文描述了归一化自适应直接盲均衡,并提出了将该方法与传统方法相结合的方法。该方法的特点是:(1)利用数据最小二乘法(Data Least square, DLS)[1]实现了高性能稳定的均衡器估计;(2)实现了高收敛速率和高恢复性能。通过计算机仿真验证了该方法的有效性。
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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