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Many communications and sensing applications hinge on the detection of asignal in a noisy, interference-heavy environment. Signal processing theoryyields techniques such as the generalized likelihood ratio test (GLRT) toperform detection when the received samples correspond to a linear observationmodel. Numerous practical applications exist, however, where the receivedsignal has passed through a nonlinearity, causing significant performancedegradation of the GLRT. In this work, we propose prepending the GLRT detectorwith a neural network classifier capable of identifying the particularnonlinear time samples in a received signal. We show that pre-processingreceived nonlinear signals using our trained classifier to eliminateexcessively nonlinear samples (i) improves the detection performance of theGLRT on nonlinear signals and (ii) retains the theoretical guarantees providedby the GLRT on linear observation models for accurate signal detection.

A Neural Network-Prepended GLRT Framework for Signal Detection Under Nonlinear Distortions