基于神经网络的GLRT非线性失真信号检测框架
A Neural Network-Prepended GLRT Framework for Signal Detection Under Nonlinear Distortions
摘要：
许多通信和传感应用都依赖于在嘈杂、干扰严重的环境中检测信号。当接收的样本对应于线性观测模型时，信号处理理论产生诸如广义似然比测试（GLRT）的技术来执行检测。然而，存在许多实际应用，其中接收信号已经通过非线性，导致GLRT的显著性能退化。在这项工作中，我们建议前置GLRT检测器与神经网络分类器能够识别特定的非线性时间样本中接收到的信号。我们表明，预处理接收到的非线性信号，使用我们训练的分类器，以消除过度的非线性样本（i）提高了非线性信号的GLRT的检测性能和（ii）保留的理论保证GLRT的线性观测模型，准确的信号检测。
英文摘要：
Many communications and sensing applications hinge on the detection of a signal in a noisy, interference-heavy environment. Signal processing theory yields techniques such as the generalized likelihood ratio test (GLRT) to perform detection when the received samples correspond to a linear observation model. Numerous practical applications exist, however, where the received signal has passed through a nonlinearity, causing significant performance degradation of the GLRT. In this work, we propose prepending the GLRT detector with a neural network classifier capable of identifying the particular nonlinear time samples in a received signal. We show that pre-processing received nonlinear signals using our trained classifier to eliminate excessively nonlinear samples (i) improves the detection performance of the GLRT on nonlinear signals and (ii) retains the theoretical guarantees provided by the GLRT on linear observation models for accurate signal detection.
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