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# Modulation Recognition Demo # Modulation Recognition Demo
RIA Hub Workflows is an automation platform built into RIA Hub. This project contains an example machine learning RIA Hub Workflows is an automation platform integrated into RIA Hub. This project provides an example machine learning
workflow for the problem of signal modulation classification. It also serves as an excellent introduction to workflow for signal modulation classification, offering a practical introduction to RIA Hub Workflows
RIA Hub Workflows.
## 📡 The machine learning development workflow ## 📡 The machine learning development workflow
@ -10,24 +9,24 @@ RIA Hub Workflows.
The development of intelligent radio solutions involves multiple steps: 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 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 training examples. Finally, we need to perform any required data preprocessing dataset curation to extract and qualify examples. Finally, we need to perform any required data preprocessing—such as
—such as augmentation—and split the dataset into training and test sets. augmentation—and split the dataset into training and test sets.
2. Secondly, we need to design and train a model. This is often an iterative process and can leverage techniques like 2. Secondly, we need to design and train a model. This is typically an iterative process, often accelerated using
Neural Architecture Search (NAS) and hyperparameter optimization to automate finding a suitable model structure and techniques such as Neural Architecture Search (NAS) and hyperparameter optimization (HPO), which help automate the
optimal hyperparameter configuration, respectively. 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. 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 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 substeps including model real-world conditions. Building an inference application typically involves several steps including model
optimization, packaging and integration, and monitoring and logging. 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 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 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- and low-code solution for the end-to-end development of intelligent radio where we come in. RIA Hub offers a no-code and low-code solution for automating the end-to-end development of
systems, allowing for a sharper focus on innovation. intelligent radio systems.
## ▶️ RIA Hub Workflows ## ▶️ RIA Hub Workflows
@ -36,24 +35,33 @@ One of the core principles of RIA Hub is Workflows, which allow users to run job
You can create workflows in one of two ways: You can create workflows in one of two ways:
- Writing YAML and placing it in the special `.riahub/workflows/` directory in your repository. - 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 - 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. 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 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 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](https://docs.gitea.com/usage/actions/overview) (similar to [GitHub Actions](https://github.com/features/actions)), providing a
familiar syntax and allowing you to leverage a wide range of third-party Actions.
## ⚙️ Qoherent-hosted runners ## ⚙️ Qoherent-hosted runners
Qoherent-hosted runners are job containers that Qoherent provides and manages to run your workflows and jobs in RIA Hub Qoherent-hosted runners are workflow runners that Qoherent provides and manages to run your workflows and jobs in
Workflows. RIA Hub Workflows.
Why use GitHub-hosted runners? Why use Qoherent-hosted runners?
- Easy to set up and start running workflows quickly, without the need to set up your own infrastructure. - Start running workflows right away, without the need to set up your own infrastructure.
- Qoherent maintains runners equipped with access to common hardware and tools for radio ML development, including - Qoherent maintains runners equipped with access to hardware and tools common for radio ML development, including
SDR testbeds and common embedded targets. SDR testbeds and common embedded targets.
If you want to learn more about the runners we have available, please feel free to reach out. We can also provide If you want to learn more about the runners we have available, [contact us](https://www.qoherent.ai/contact/) directly. We can also provide
custom runners equipped with specific radio hardware and RAN software upon request. 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. Want to register your own runner? No problem! Please refer to the RIA Hub documentation for more details.
@ -61,6 +69,18 @@ Want to register your own runner? No problem! Please refer to the RIA Hub docume
## 🔍 Modulation Recognition ## 🔍 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 ## 🚀 Getting started
@ -69,44 +89,61 @@ Want to register your own runner? No problem! Please refer to the RIA Hub docume
2. Enable Workflows (*Settings → Advanced Settings → Enable Repository Actions*). 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. If no runners 3. Check for available runners. The runner management tab can found at the top of the 'Workflows' tab in your
are available, you'll need to register one before proceeding. repository. If no runners are available, you'll need to register one before proceeding.
4. Clone down the project. For example: 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:
```commandline ```commandline
git clone https://git.riahub.ai/user/modrec-workflow.git git clone https://git.riahub.ai/user/modrec-workflow.git
cd modrec-workflow cd modrec-workflow
``` ```
5. Set the workflow runner in `.riahub/workflows/workflow.yaml`. The runner is set on line 13: 6. Set the workflow runner in `.riahub/workflows/workflow.yaml`. The runner is set on line 13:
```yaml ```yaml
runs-on: ubuntu-latest 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 **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. the workflow, but we suggest reducing the number of training epochs to keep runtime reasonable.
6. (Optional) Configure the workflow. All parameters—including file paths, model architecture, and training 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? The default configuration is suitable for getting started. settings—are set in `conf/app.yaml`. Want to jump right in? No problem, the default configuration is suitable.
7. Push changes. This will start the workflow automatically. 8. Push changes. This will automatically trigger the workflow. You can monitor workflow progress under the 'Workflows'
tab in the repository.
8. Inspect the workflow output. You can expand and collapse individual steps to view their terminal output. A check 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. mark indicates that the step completed successfully.
9. Inspect the workflow artifacts. Additional information on workflow artifacts can be found in the next section. 10. Inspect the workflow artifacts. Additional information on workflow artifacts can be found in the next section.
## Workflow artifacts ## Workflow artifacts
The example generates several workflow artifacts, including: This workflow generates several artifacts, including:
- `recordings`: Folder of synthetic signal recordings.
- `dataset`: The training and validation datasets: `train.h5` and `val.h5`, respectively. - `dataset`: The training and validation datasets: `train.h5` and `val.h5`, respectively.
@ -121,18 +158,22 @@ stages of training.
by [ONNX Runtime](https://onnxruntime.ai/) for more efficient loading and execution.) by [ONNX Runtime](https://onnxruntime.ai/) for more efficient loading and execution.)
- `profile-data`: Model execution traces, in JSON format. - `profile-data`: Model execution traces, in JSON format. See the section below for instructions on how to inspect the
trace using Perfetto.
- `recordings`: Folder of synthesised signal recordings. ## 📊 Inspecting the model trace using Perfetto
[Perfetto](https://ui.perfetto.dev/) 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 ## 🤝 Contribution
We welcome contributions from the community! Whether it's an enhancement, bug fix, or new how-to guide, your 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](https://www.qoherent.ai/contact/) directly, we're looking forward to collaborating with input is valuable. To get started, please [contact us](https://www.qoherent.ai/contact/) directly, we're looking forward to collaborating with
you. 🚀 you. 🚀
@ -158,57 +199,3 @@ This example is **free and open-source**, released under [AGPLv3](https://www.gn
Alternative licensing options are available. Alternative licensing options are available. Please [contact us](https://www.qoherent.ai/contact/) Alternative licensing options are available. Alternative licensing options are available. Please [contact us](https://www.qoherent.ai/contact/)
for further details. for further details.
### Configure GitHub Secrets
Before running the pipeline, add the following repository secrets in GitHub (Settings → Secrets and variables → Actions):
- **RIAHUB_USER**: Your RIA Hub username.
- **RIAHUB_TOKEN**: RIA Hub access token with `read:packages` scope (from your RIA Hub account **Settings → Access Tokens**).
- **CLONER_TOKEN**: Personal access token for `stark_cloner_bot` with `read_repository` scope (from your on-prem Git server user settings).
Once secrets are configured, you can run the pipeline:
3.
## How to View the JSON Trace File
- Captures a full trace of model training and inference performance for profiling and debugging
- Useful for identifying performance bottlenecks, optimizing resource usage, and tracking metadata
-
Access this [link](https://ui.perfetto.dev/)
Click on Open Trace File -> Select your specific JSON trace file
Explore detailed visualizations of performance metrics, timelines, and resource usage to diagnose bottlenecks and optimize your workflow.
## Submiting Issues
Found a bug or have a feature request?
Please submit an issue via the GitHub Issues page.
When reporting bugs, include:
Steps to reproduce
- Error logs and screenshots (if applicable)
- Your app.yaml configuration (if relevant)
## Developer Details
Coding Guidelines:
Follow PEP 8 for Python code style.
Include type annotations for all public functions and methods.
Write clear docstrings for modules, classes, and functions.
Use descriptive commit messages and reference issue numbers when relevant.
Contributing
All contributions must be reviewed via pull requests.
Run all tests and ensure code passes lint checks before submission.