Training Data Augmentation for Deep Learning Radio Frequency Systems

• URL: http://arxiv.org/abs/2010.00178v4
• Date: Mon, 4 Jan 2021 15:50:24 GMT
• Title: Training Data Augmentation for Deep Learning Radio Frequency Systems
• Authors: William H. Clark IV, Steven Hauser, William C. Headley, and Alan J. Michaels
• Abstract summary: This work focuses on the data used during training. In general, the examined data types each have useful contributions to a final application. Despite the benefit of captured data, the difficulties and costs that arise from live collection often make the quantity of data needed to achieve peak performance impractical.
• Score: 1.1199585259018459
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Applications of machine learning are subject to three major components that contribute to the final performance metrics. Within the category of neural networks, and deep learning specifically, the first two are the architecture for the model being trained and the training approach used. This work focuses on the third component, the data used during training. The primary questions that arise are ``what is in the data'' and ``what within the data matters?'' Looking into the Radio Frequency Machine Learning (RFML) field of Automatic Modulation Classification (AMC) as an example of a tool used for situational awareness, the use of synthetic, captured, and augmented data are examined and compared to provide insights about the quantity and quality of the available data necessary to achieve desired performance levels. There are three questions discussed within this work: (1) how useful a synthetically trained system is expected to be when deployed without considering the environment within the synthesis, (2) how can augmentation be leveraged within the RFML domain, and lastly, (3) what impact knowledge of degradations to the signal caused by the transmission channel contributes to the performance of a system. In general, the examined data types each have useful contributions to a final application, but captured data germane to the intended use case will always provide more significant information and enable the greatest performance. Despite the benefit of captured data, the difficulties and costs that arise from live collection often make the quantity of data needed to achieve peak performance impractical. This paper helps quantify the balance between real and synthetic data, offering concrete examples where training data is parametrically varied in size and source.

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