Systems and methods for modulation classification of baseband signals using multiple data representations of signal samples

A system for classifying baseband signals derived from radio frequency signals with respect to modulation type, comprising:

• a consolidated neural network whose objective is modulation classification performance, the consolidated neural network comprising a plurality of parallel neural networks;
• and
• a pre-processing stage configured to:
• receive 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, each sample including real and imaginary parts of the baseband signal of the unknown modulation type in the complex plane;
• generate a plurality of different data representations of the complex quadrature vector of interest;
• and
• provide each data representation of the complex quadrature vector of interest to a respective one of the plurality of parallel neural networks of the consolidated neural network;
• wherein the consolidated neural network is configured to generate, dependent on the data representations of the complex quadrature vector of interest provided to the respective ones of the plurality of parallel neural networks, a classification result for the baseband signal derived from the radio frequency signal of the unknown modulation type, the classification result including a final output vector in which each element indicates a probability that the unknown modulation type is a respective one of a plurality of known modulation types.