二阶循环平稳随机过程的属性化及其在信号存在检测中的应用

Jeong Ho Yeo, Joon Ho Cho
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引用次数: 7

摘要

本文证明了二阶循环平稳(SOCS)随机过程,无论它是合适的还是不合适的,都可以转化为具有两倍周期的等价的正复SOCS随机过程。提出了一种简单的线性-共轭线性周期性时变算子,称为频移(FRESH)属性器来完成这种转换。作为一个应用,我们考虑了一个不适当复杂的SOCS随机过程的存在检测,它很好地模拟了数字调制信号的复杂包络,如脉冲幅度调制(PAM)、交错四元相移键控(SQPSK)、高斯最小移键控(GMSK)等。特别是,针对非复杂SOCS高斯随机过程,推导了利用FRESH属性器的最优存在检测器,该检测器提供了检测误差概率的下界。推导出的最优检测器在结构上具有优势,它由一个FRESH properizer和一个线性滤波器组成,与传统的由平行连接的线性和共轭线性滤波器组成的检测器具有相同的性能。并给出了数值结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Properization of second-order cyclostationary random processes and its application to signal presence detection
In this paper, we show that a second-order cyclo-stationary (SOCS) random process, whether it is proper or improper, can be always converted to an equivalent proper-complex SOCS random process with twice the cycle period. A simple linear-conjugate linear periodically time-varying operator called a FREquency SHift (FRESH) properizer is proposed to perform this conversion. As an application, we consider the presence detection of an improper-complex SOCS random process, which well models the complex envelopes of digitally modulated signals such as pulse amplitude modulation (PAM), staggered quaternary phase-shift keying (SQPSK), Gaussian minimum shift keying (GMSK), etc. In particular, the optimal presence detector that utilizes the FRESH properizer is derived for improper-complex SOCS Gaussian random processes, which provides the lower bound on the detection error probabilities. The derived optimal detector, which has the structural advantage in that it consists of a FRESH properizer followed by a single linear filter, achieves the same performance as the conventional detector that consists of parallel-connected linear and conjugate-linear filters. Numerical results are also provided.
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