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Classification of Digital Amplitude-Phase Modulated Signals in Time-Correlated Non-Gaussian Channels

机译：时间相关的非高斯信道中数字调相信号的分类

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摘要

In this paper, a new algorithm is proposed for the classification of digital amplitude-phase modulated signals in flat fading channels with time-correlated non-Gaussian noise. The first-order statistics of the additive noise is modeled by a Gaussian mixture distribution and an autoregressive (AR) process is used to model the time-correlation. The proposed classifier involves the use of a whitening filter, necessary to reduce the complexity of the classification process, and maximum-likelihood classification. For the estimation of the whitening filter coefficients, a new blind technique that is based on the use of a robust H_∞ filter is developed. After whitening the received signal, following a composite hypothesis testing approach, the unknown fading and noise distribution parameters are estimated. Results are presented which show that when the noise process is time-correlated non-Gaussian, the proposed classifier outperforms maximum-likelihood classifiers developed under the assumption that the noise process is either white non-Gaussian or white Gaussian. It is also shown that when the noise process is white Gaussian, the proposed classifier's performance closely approaches that of the maximum-likelihood classifier developed for white Gaussian noise channels.
机译：本文提出了一种新算法，用于对时间相关的非高斯噪声的平坦衰落信道中的数字幅度相位调制信号进行分类。加性噪声的一阶统计量通过高斯混合分布进行建模，并且使用自回归（AR）过程对时间相关性进行建模。提出的分类器涉及使用白化滤波器，这对于降低分类过程的复杂性和最大似然分类是必需的。为了估算白化滤波器系数，开发了一种新的基于鲁棒H_∞滤波器的盲技术。在对接收到的信号进行白化之后，采用一种复合假设测试方法，可以估算未知的衰落和噪声分布参数。结果表明，当噪声过程是时间相关的非高斯分布时，在噪声过程是白色非高斯分布或白色高斯分布的假设下，拟议的分类器优于最大似然分类器。还表明，当噪声过程是白高斯噪声时，所提出的分类器的性能接近于为白高斯噪声信道开发的最大似然分类器的性能。

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