A closed-form solution to blind equalization
A closed-form solution to blind equalization
复制标题
盲均衡的封闭式解决方案
DOI：
10.1016/0165-1684(94)90026-4
复制
发表时间：
1994
期刊：
影响因子：
--
通讯作者：
K. Kammeyer
中科院分区：
文献类型：
--
作者：
B. Jelonnek;K. Kammeyer
近年来，一些文献提出了新的盲自适应均衡算法。这些算法是基于随机梯度法，因此可以被视为一个“盲”的对应经典的LMS（最小均方）算法。众所周知，这些算法表现出相对较慢的收敛速度。快速收敛的经典解决方案是RLS（递归最小二乘）算法，它利用了封闭形式的解决方案。本文的目的是推导出一个封闭形式的解决方案的意义下的盲均衡。它将被示出的均衡器系数可以唯一地从接收信号的特定的4阶累积量矩阵的特征向量导出。通过一些例子，将证明特征向量解接近理想的均方误差（MSE）解。
In some recent papers new algorithms for blind adaptive equalization were proposed. These algorithms are based on the stochastic gradient method and thus can be regarded as a ‘blind’ counterpart to the classic LMS (least mean squares) algorithms. It is well known that these algorithms show relatively slow convergence speed. The classic solution to get fast convergence is the RLS (recursive least squares) algorithm which makes use of the closed-form solution. The purpose of this paper is to derive a closed-form solution in the sense of blind equalization. It will be shown that the equalizer coefficients can be uniquely derived from the eigenvectors of a specific 4th-order cumulant matrix of the received signal. By means of some examples it will be demonstrated that the eigenvector solution is near the ideal MSE (mean square error) solution.