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Automatic Modulation Classification using a Waveform Signature
This paper develops a novel blind modulation classification algorithm, which uses a set of higher order statistics to overcome these challenges.
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.
When is Enough Enough? "Just Enough" Decision Making with Recurrent Neural Networks for Radio Frequency Machine Learning
Prior work has demonstrated that recurrent neural network architectures show promising improvements over other machine learning architectures when processing temporally correlated inputs, such as wireless communication signals.
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.
