Deep learning-based RF modulation classifiers areoften deployed as single, “generalist” models trained over a largemix of signal types, bands, and impairments. In practice, however,different architectures excel on different families of modulations:spectral CNNs shine on narrowband constellations, recurrentmodels track slowly time-varying analog signals, and transformerstyle feature fusion can exploit joint IQ+FFT structure.This paper studies a simple … Continue reading Specialized Models per Modulation Family: Routing Subsets to SpectralCNN, SignalLSTM, ResNetRF,and SignalTransformer