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We introduce learned attention models into the radio machine learning domainfor the task of modulation recognition by leveraging spatial transformernetworks and introducing new radio domain appropriate transformations. Thisattention model allows the network to learn a localization network capable ofsynchronizing and normalizing a radio signal blindly with zero knowledge of thesignals structure based on optimization of the network for classificationaccuracy, sparse representation, and regularization. Using this architecture weare able to outperform our prior results in accuracy vs signal to noise ratioagainst an identical system without attention, however we believe such anattention model has implication far beyond the task of modulation recognition.

Radio Transformer Networks: Attention Models for Learning to Synchronize in Wireless Systems