SYSTEMS AND METHODS FOR MODULATION CLASSIFICATION OF BASEBAND SIGNALS USING ATTENTION-BASED LEARNED FILTERS

摘要

Systems and methods for classifying baseband signals include receiving, at a pre-processing stage of a neural network whose objective is modulation classification performance, a complex quadrature vector of interest including a plurality of samples of a baseband signal derived from a radio frequency signal of an unknown modulation type, providing the vector of interest to a plurality of FIR filters, each of which outputs a respective intermediate filtered version of the vector of interest, combining the outputs of two or more of the FIR filters to produce a filtered version of the vector of interest, including applying respective weightings to the outputs of the FIR filters, and providing the filtered version of the vector of interest to an analysis stage of the neural network for classification with respect to a plurality of known modulation types. The neural network may apply attention-based selection to learn the filters and respective weightings.