论文标题
在非线性畸变下用于信号检测的神经网络拟合GLRT框架
A Neural Network-Prepended GLRT Framework for Signal Detection Under Nonlinear Distortions
论文作者
论文摘要
许多通信和传感应用取决于在嘈杂，干扰的环境中检测信号的检测。信号处理理论产生的技术（例如广义似然比检验（GLRT））在接收样品对应于线性观察模型时执行检测。但是，存在许多实际应用，其中收到的信号已经通过非线性，从而导致GLRT的显着性能降解。在这项工作中，我们建议使用能够在接收信号中识别特定的非线性时间样本的神经网络分类器预先准备GLRT检测器。我们表明，预处理使用我们训练的分类器接收到非线性信号以消除过度非线性样品（i）改善GLRT在非线性信号上的检测性能，并且（ii）保留GLRT在线性观察模型上提供的理论保证，以进行准确的信号检测。
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.
