Modulation Recognition Demo

RIA Hub Workflows is an automation platform integrated into RIA Hub. This project provides an example machine learning workflow for signal modulation classification, offering a practical introduction to RIA Hub Workflows

📡 The machine learning development workflow

The development of intelligent radio solutions involves multiple steps:

1. First, we need to prepare a machine learning-ready dataset. This involves signal synthesis or capture, followed by dataset curation to extract and qualify examples. Finally, we need to perform any required data preprocessing—such as augmentation—and split the dataset into training and test sets.

2. Secondly, we need to design and train a model. This is typically an iterative process, often accelerated using techniques such as Neural Architecture Search (NAS) and hyperparameter optimization (HPO), which help automate the discovery of an effective model structure and optimal hyperparameter settings.

3. Once a machine learning model has been trained and validated, the next step is to build an inference application. This step transforms the model from a research artifact into a practical tool capable of making predictions in real-world conditions. Building an inference application typically involves several steps including model optimization, packaging and integration, and monitoring and logging.

This is a lot of work, and much of it involves tedious software development and repetitive tasks, like setting up and configuring infrastructure. What's more? There is a shortage of domain expertize in ML and MLOps for radio. That's where we come in. RIA Hub offers a no-code and low-code solution for automating the end-to-end development of intelligent radio systems.

▶️ RIA Hub Workflows

One of the core principles of RIA Hub is Workflows, which allow users to run jobs in isolated Docker containers.

You can create workflows in one of two ways:

• Writing YAML and placing it in the special .riahub/workflows/ directory in your repository.

• Using RIA Hub's built-in tools for Dataset Management, Model Building, and Application Development, which will automatically generate the YAML workflow file(s) for you.

Workflows can be configured to run automatically on push and pull request events. You can monitor and manage running workflows in the 'Workflows' tab in your repository.

Workflows require a runner, which retrieves job definitions from RIA Hub, executes them in isolated containers, and reports the results back to RIA Hub. The next section outlines the convenience and advantage of using Qoherent-hosted runners. The workflow configuration defines the specifications and settings of the available job containers.

The best part? RIA Hub Workflows are built on Gitea Actions (similar to GitHub Actions), providing a familiar syntax and allowing you to leverage a wide range of third-party Actions.

⚙️ Qoherent-hosted runners

Qoherent-hosted runners are workflow runners that Qoherent provides and manages to run your workflows and jobs in RIA Hub Workflows.

Why use Qoherent-hosted runners?

• Start running workflows right away, without the need to set up your own infrastructure.
• Qoherent maintains runners equipped with access to hardware and tools common for radio ML development, including SDR testbeds and common embedded targets.

If you want to learn more about the runners we have available, contact us directly. We can also provide custom runners equipped with specific radio hardware and RAN software upon request.

Want to register your own runner? No problem! Please refer to the RIA Hub documentation for more details.

🔍 Modulation Recognition

In radio, the modulation scheme refers to the method used to encode information onto a carrier signal. Common schemes such as BPSK, QPSK, and QAM vary the amplitude, phase, or frequency of the signal in structured ways to represent digital data. These schemes are fundamental to nearly all wireless communication systems, enabling efficient and reliable transmission over different channels and under various noise conditions.

Machine learning-based modulation classification helps identify which modulation scheme is being used, especially in scenarios where prior knowledge of the signal format is unavailable or unreliable. Traditional methods often rely on expert-designed features and rule-based algorithms, which can struggle in real-world environments with multipath, interference, or hardware impairments. In contrast, ML-based approaches can learn complex patterns directly from raw signal data, offering higher robustness and adaptability. This is particularly valuable in applications like cognitive radio, spectrum monitoring, electronic warfare, and autonomous communication systems, where accurate and fast modulation recognition is critical.

🚀 Getting started

1. Fork the project repo, using the button in the upper right hand corner.

2. Enable Workflows (Settings → Advanced Settings → Enable Repository Actions). TODO: Remove this point once default units have been updated to include actions in forks

3. Check for available runners. The runner management tab can found at the top of the 'Workflows' tab in your repository. If no runners are available, you'll need to register one before proceeding.

4. Configure Git API credentials, if not suitable credentials are already set. This is required for accessing Utils in the job container. This requires three steps:

   • Create a personal access token with the following permissions: read:packages (User Settings → Applications → Manage Access Tokens).

   • Create a Workflow Variable RIAHUB_USER with your RIA Hub username (Repo Settings → Actions → Variables Management)

   • Create a Workflow Secret RIAHUB_TOKEN with the token created above (Repo Settings → Actions → Secrets Management)

   TODO: Remove this point once the Utils wheel file has been added to this project.

5. Clone down the project. For example:

git clone https://git.riahub.ai/user/modrec-workflow.git
cd modrec-workflow

6. Set the workflow runner in .riahub/workflows/workflow.yaml. The runner is set on line 13:

runs-on: ubuntu-latest-2080

Note: We recommend running this demo on a GPU-enabled runner. If a GPU runner is not available, you can still run the workflow, but we suggest reducing the number of training epochs to keep runtime reasonable.

7. (Optional) Configure the workflow. All parameters—including file paths, model architecture, and training settings—are set in conf/app.yaml. Want to jump right in? No problem, the default configuration is suitable.

8. Push changes. This will automatically trigger the workflow. You can monitor workflow progress under the 'Workflows' tab in the repository.

9. Inspect the workflow output. You can expand and collapse individual steps to view terminal output. A check mark indicates that the step completed successfully.

10. Inspect the workflow artifacts. Additional information on workflow artifacts can be found in the next section.

Workflow artifacts

This workflow generates several artifacts, including:

• recordings: Folder of synthetic signal recordings.

• dataset: The training and validation datasets: train.h5 and val.h5, respectively.

• checkpoints: Saved model checkpoints. Each checkpoint contains the model’s learned weights at various stages of training.

• onnx-file: The trained model as an ONNX graph.

• ort-file: Model in .ORT format, recommended for edge deployments. (.ORT files are optimized and serialized by ONNX Runtime for more efficient loading and execution.)

• profile-data: Model execution traces, in JSON format. See the section below for instructions on how to inspect the trace using Perfetto.

📊 Inspecting the model trace using Perfetto

Perfetto is an open-source trace visualization tool developed by Google. It provides a powerful web-based interface for inspecting model execution traces. Perfetto is especially useful for identifying bottlenecks.

To inspect model trace, navigate to Perfetto. Select Navigation → Open trace file, and choose the JSON trace file includes in the profile-data artifact.

🤝 Contribution

We welcome contributions from the community! Whether it's an enhancement, bug fix, or new tutorial, your input is valuable. To get started, please contact us directly, we're looking forward to collaborating with you. 🚀

If you encounter any issues or to report a security vulnerability, please submit a bug report.

Qoherent is dedicated to fostering a friendly, safe, and inclusive environment for everyone. For more information on our commitment to diversity, please refer to our Diversity Statement.

We kindly insist that all contributors review and adhere to our Code of Conduct and that all code contributors review our Coding Guidelines.

🖊️ Authorship

This demonstration was developed by Liyu Xiao during his summer co-op term at Qoherent.

If you like this project, don’t forget to give it a star! ⭐

📄 License

This example is free and open-source, released under AGPLv3.

Alternative licensing options are available. Alternative licensing options are available. Please contact us for further details.