Learning and Visualizing Modulation Discriminative Radio Signal Features
Space and Naval Warfare Systems Center Pacific San Diego United States
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In this work we explore the adaptation of convolutional autoencoders to complex-valued temporal domain radio signals. We propose a method for accomplishing online semi-supervised learning with a tied-weight convolutional autoencoder applied to a modulation classification task and provide some initial results. We also demonstrate a novel application of class activation maps CAMs to obtain interpretable visualizations of modulation-discriminative temporal structure in input signals. Finally, we show that our visualization method may be successfully applied to pre-trained models with negligible impact on classification performance on an automated modulation classification AMC task. This work was done as part of the BIAS Biologically Inspired Autonomous Sensing project, funded from the Naval Innovative Science and Engineering NISE Program.
- Operations Research
- Computer Systems Management and Standards