SYSTEMS AND METHODS FOR MODULATION CLASSIFICATION OF BASEBAND SIGNALS USING MULTIPLE DATA REPRESENTATIONS OF SIGNAL SAMPLES

Systems and methods for classifying baseband signals with respect to modulation type include receiving, at a consolidated neural network whose objective is modulation classification performance, a complex quadrature vector of interest including multiple samples of a baseband signal derived from a radio frequency signal of unknown modulation type, generating multiple data representations of the vector of interest, providing each data representation to one of multiple parallel neural networks in the consolidated neural network, and receiving a classification result for the baseband signal based on combined outputs of the parallel neural networks. The consolidated neural network may be trained to classify baseband signals with respect to known modulation types by receiving complex quadrature training vectors, each including samples of a baseband signal derived from a radio frequency signal of known modulation type, and comparing a classification result for the training vector to the known modulation type to determine modulation classification performance.