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An Analysis of RF Transfer Learning Behavior Using Synthetic Data
Transfer learning (TL) techniques, which leverage prior knowledge gained from data with different distributions to achieve higher performance and reduced training time, are often used in computer vision (CV) and natural language processing (NLP), but have yet to be fully utilized in the field of radio frequency machine learning (RFML).
Assessing the Value of Transfer Learning Metrics for RF Domain Adaptation
The use of transfer learning (TL) techniques has become common practice in fields such as computer vision (CV) and natural language processing (NLP).
Quantifying and Extrapolating Data Needs in Radio Frequency Machine Learning
While the model's deployed performance is dependent on numerous variables within the scope of machine learning, beyond that of the training data itself, the effect of the dataset is isolated in this work to better understand the role training data plays in the problem.
The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications
While deep machine learning technologies are now pervasive in state-of-the-art image recognition and natural language processing applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications.
Training Data Augmentation for Deep Learning Radio Frequency Systems
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.
