AirPack Overview

AirPack is an introductory framework for creating deep learning models to classify radio frequency (RF) signals and deploying the model to Deepwave Digital’s Artificial Intelligence Radio Transceiver (AIR-T). It provides tools for training a neural network on 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. 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.

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.

It’s easy to get started: after running one of the provided training examples using the bundled dataset, the software will produce an RF signal classifier neural network that is deployable on the AIR-T software defined radio. In order to train the model within a reasonable period of time, you’ll need to install AirPack’s Docker container for training on a separate PC with a NVidia GPU.

After you are up and running, you can tweak parameters of the existing classifier models, re-train them using your own signal data, or even modify the structure of the classifier to suit your needs.

The following instructions walk though the process of installing the AirPack software on the training computer with a Docker container, training a CNN within the Docker container, installing the trained model on the AIR-T, and then performing inference with the trained model on the AIR-T.

General Workflow

You may refer to the Table of Contents menu for instructions on each step below.

  • Step 1: Install AirPack on the training computer.

  • Step 2: Train the model. This will produce a .onnx file (recommended) or a .ufffile (legacy) of the trained model. You may also test the trained model on the training computer.

  • Step 3: Install AirPack on the AIR-T and copy the trained model (.onnx file) to the AIR-T.

  • Step 4: Run inference on the AIR-T using the provided code which will automatically:

    • Optimize the .onnx file using TensorRT into a .plan file.

    • Setup the radio.

    • Receive RF samples from the radio and send them to the DNN for inference.

    • Print the resulting classification output.

  • Step 5: Now you can modify and tune AirPack for your specific applications. This may include:


This software package is provided by Deepwave Digital, Inc.

For more information on this and other products, please visit our website at

For AirPack customer support, please fill out a Customer Support Request on our developer portal. Please note that to submit support requests, you must have a portal account with an active support and maintenance subscription.