An introductory deep learning model creation framework for radio frequency (RF) signals on the Artificial Intelligence Radio Transceiver (AIR-T).
AirPack contains everything you need, including deep learning source code and training data, to walk you through the crucial steps of training, testing, and deploying a convolutional neural network (CNN) to detect and classify RF signals on Deepwave Digital's Artificial Intelligence Radio Transceiver (AIR-T). It provides tools for training a neural network on provided RF signal data (including source code for a convolutional neural network classifier (CNN) model), tools for developing your own models, bundled training datasets, and examples of deploying a trained model on the AIR-T for inference.
The AirPack tools are accessible through a Python API that allows the user to call the functions as needed, either from one of our provided examples in the
airpack_scripts package, or by directly importing the
airpack package from a Python console or Jupyter notebook. In order to simplify the hassle of installing all the complicated drivers, files, and toolboxes to build a machine learning environment, AirPack also provides a custom Docker container for use on a training PC. For deployment on the AIR-T, AirPack provides an inference script in the
airpack_scripts package to initialize the radio, receive RF samples and send them to the classifier running on the embedded GPU (via zero-copy), and perform signal classification.
By using AirPack, the engineering development and network training schedules can be shortened, leading to a reduction in labor costs and faster time to market.
View the AirPack Documentation and API website.