Deep learning at the edge: automatic modulation classification on real world signals

Shane MacDonald; Lucas Torlay; Hyatt Baker

doi:10.1117/12.2585787

12 April 2021 Deep learning at the edge: automatic modulation classification on real world signals

Shane MacDonald, Lucas Torlay, Hyatt Baker

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117462S (2021) https://doi.org/10.1117/12.2585787
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Conference Poster

Abstract

In this paper, we present an end-to-end pipeline for deep learning applied to Automatic Modulation Classification (AMC). We begin by utilizing Modulation Classification Network (MCNET), a recently published cost-efficient convolutional neural network (CNN) with skip connections. Model efficacy is confirmed and the algorithm is advanced with hyper parameter and regularization adjustments, transfer learned with an augmented over-the-air data set, and then a computationally superior version is deployed to an edge device. The model is initially trained with the well-known 2018 DEEPSIG data set that includes 24 modulation schemes. Transfer learning utilizes the Experiments, Scenarios, Concept of Operations, and Prototype Engineering (ESCAPE) data set. The edge node device utilized, but is not limited to, an NVIDIA Jetson AGX XAVIER. Under ideal conditions, classification at the edge node resulted in 96% accuracy with 11 over-the-air modulation schemes. Inferences at the edge were up to 13 times faster than the non-optimized model.

Conference Presentation

Citation Download Citation

Shane MacDonald, Lucas Torlay, and Hyatt Baker "Deep learning at the edge: automatic modulation classification on real world signals", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117462S (12 April 2021); https://doi.org/10.1117/12.2585787

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