Gain-Control-Free Near-Efficient Phase Acquisition for QAM Constellations
Gain-Control-Free Near-Efficient Phase Acquisition for QAM Constellations
复制标题
DOI：
10.1109/tsp.2008.917035
复制
发表时间：
2008-07
影响因子：
5.4
通讯作者：
G. Panci;S. Colonnese;S. Rinauro;G. Scarano
中科院分区：
文献类型：
--
作者：
G. Panci;S. Colonnese;S. Rinauro;G. Scarano
介绍了一种新的非数据辅助的正交幅度调制(QAM)信号的相移估计器。与近乎有效的现有相位捕获技术相反，这种估计器不需要初步的增益调整阶段，同时其精度在中高信噪比(SNR)范围内保持了Cramer-Rao界限的斜率，其中它通常优于现有的盲估计器，对于密集和交叉QAM星座具有显著的改善。此外，它只需要对SNR进行非常粗略的估计。与其他无增益控制的盲相偏估计器一样，它测量(四重)相位概率密度函数(Pdf)在未知相偏下旋转的循环移位量。首先通过用典型的相位直方图过程测量接收的数据相位pdf，然后通过在测量的相位直方图和在零相偏假设内评估的参考相位pdf之间基于循环互相关的过程来估计相位偏移引起的循环移位，来进行对相位偏移引起的循环移位的估计。实际上，该估计过程是以考虑接收数据的合适的非线性变换的二维(幅度/相位)pdf的层析投影的广义版本给出的。层析投影对pdf执行幅度加权，这进而导致改进的总体估计精度，如这里为评估估计器性能而执行的理论分析和数值模拟所示。
This paper introduces a novel, not data aided, phase-offset estimator for quadrature amplitude modulated (QAM) signals. Contrarily to near-efficient existing phase acquisition techniques, this estimator does not require a preliminary gain adjustment stage while its accuracy preserves the slope of Cramer-Rao bound for medium-high signal-to-noise ratio (SNR) ranges, where it typically outperforms existing blind estimators, with significant improvement for dense and cross QAM constellations. Moreover, it needs only a very rough estimate of the SNR. Like other gain-control-free blind phase-offset estimators, it measures the amount of the cyclic shift by which the (four-folded) phase probability density function (pdf) is rotated under an unknown phase-offset. Estimation of the phase-offset-induced cyclic shift is conducted first by measuring the received data phase pdf by a canonical phase histogram procedure, then by estimating the phase-offset-induced cyclic shift through a cyclic cross correlation-based procedure between the measured phase histogram and a reference phase pdf evaluated within the zero phase-offset hypothesis. Actually, the estimation procedure is presented in a generalized version that considers a tomographic projection of the bidimensional (magnitude/phase) pdf of suitable nonlinear transformations of the received data. The tomographic projection performs a magnitude weighing on the pdf, and this, in turn, results in an improved overall estimation accuracy, as shown by theoretical analysis and numerical simulations here performed to assess the estimator performance.