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ria-core

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Radio Intelligence Apps' open-source core, by Qoherent 📡🚀

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## ⚠️ Repository Archived

RIA Core has been rebranded as RIA Toolkit OSS, and development has moved from GitHub to RIA Hub. This repository has been archived and is no longer actively maintained.

Please visit the new homepage at https://riahub.ai/qoherent/ria-toolkit-oss, where RIA Toolkit OSS remains free and open source.

<h2 align="center">
  <br>
  <img src="./docs/source/branding/riacore.png" alt="Radio Intelligence Apps">
</h2>

<h3 align="center">Radio Intelligence Apps, By <a href="https://www.qoherent.ai/">Qoherent</a></h3>

<h4 align="center">Let's build intelligent radios together. 📡🚀</h4>

<p align="center">
  
  <a href="https://pypi.org/project/ria">
    <img src="https://img.shields.io/pypi/v/ria"/>
  </a>
  
  <a href="https://anaconda.org/conda-forge/ria">
    <img src="https://img.shields.io/conda/vn/conda-forge/ria"/>
  </a>
  
  <a href="https://www.gnu.org/licenses/agpl-3.0">
    <img src="https://img.shields.io/badge/License-AGPLv3-blue.svg" />
  </a>
  
  <a href="https://pypi.org/project/ria">
    <img src="https://img.shields.io/pypi/status/ria"/>
  </a>
  
  <a href="https://radiointelligence.io/">
    <img src="https://img.shields.io/badge/docs-ria--core-blue"/>
  </a>
</p>

RIA Core is the foundational, open-source core of [RIA Hub](https://riahub.ai/), an extensive AI development 
platform tailored for [software-defined radio](https://en.wikipedia.org/wiki/Software-defined_radio) (SDR).

RIA Core drives the creation of [intelligent radios](https://www.qoherent.ai/intelligentradio/), unlocking 
solutions in an increasingly congested, contested, and complex wireless spectrum. Learn more about RIA Core on 
our website [here](https://www.qoherent.ai/radiointelligenceapps-project/).

## 🌟 Key Features

- Synthesize modulated radio frequency (RF) recordings based on signals generated in Python, 
[GNU Radio](https://www.gnuradio.org/), or [MATLAB](https://www.mathworks.com/products/matlab.html), 
expediting prototyping and experimentation.

- Visualize and inspect recordings with [IQengine](https://iqengine.org/browser), simplifying data exploration.

- Curate RF datasets from [SigMF](https://github.com/sigmf/SigMF) recordings and 
store them in [HDF5](https://github.com/HDFGroup/hdf5) format, streamlining data management 
and enhancing accessibility.

- Accelerate model development with off-the-shelf deep learning models, tailored for radio applications, 
freeing up more time for research and development.

## 🚀 Want More RIA?

- Experience the complete, GUI-based AI development experience offered by 
[RIA Hub](https://www.qoherent.ai/radiointelligenceapps-hub/). This comprehensive solution provides an intuitive 
and feature-rich environment, empowering developers to explore, create, and optimize AI applications for SDR.

- [RIA RAN](https://www.qoherent.ai/radiointelligenceapps-ran/) allows seamless integration of high-performance ML 
inference software directly onto an open-source [gNodeB](https://inseego.com/resources/5g-glossary/what-is-gnb/), 
empowering radio engineers to leverage AI for sensing and communications.

## 🛠️ Getting Started

RIA Core is available from [PyPI](https://pypi.org/project/ria), and can be installed with pip:
```sh
pip install ria
```

RIA Core is also available from [Conda-Forge](https://anaconda.org/conda-forge/ria), and can be installed with Conda:
```sh
conda install ria
```

Interfacing with your local SDR hardware may require additional drivers and configurations.

Please refer to the [documentation](https://radiointelligence.io/) for more information on getting
started with RIA Core.

## 🐳 Docker Support

Coming soon: Docker support for building images for both CPU and GPU targets.

## 💻 Usage

RIA Core consists of importable modules that can be used within your Python projects, as well as a command-line 
interface (CLI), allowing you to execute key functionality directly from the command line.

The RIA CLI is automatically installed alongside RIA. Simply execute `ria --help` from the command line for
CLI usage information.

For example, if we wanted to curate a dataset from a collection of SigMF recordings, apply an artificial IQ 
Imbalance, and save to file as a machine-learning ready dataset, we could do this from the command line with:
```sh
ria curate --recordings 'data/recordings' --output 'data/datasets/out.h5' --phase_imbalance $pi
```

Alternatively, we could achieve the same in Python with:
```python
from math import pi
from ria import curate
from ria.impairments import iq_imblance

# Curate a radio dataset from a collection of SigMF recordings.
dataset = curate(recordings='data/recordings')

# Apply an artificial IQ Imbalance.
dataset = iq_imblance(dataset=dataset, phase_imbalance=pi)

# Save the dataset to file.
dataset.to_h5('data/datasets/out.h5')
```

We can proceed to train a 4G LTE / 5G NR classifier using this dataset, and save the trained model to file
in ONNX format:
```sh
ria train --train_dataset 'data/datasets/out.h5' --model 'LTE_NR_CLassifier' --batchsize 4 --to_onnx 'models/classifier.onnx'
```

Equivalently, in Python:
```python
from ria.models import LTE_NR_CLassifier
from pytorch_lightning import Trainer
from torch.utils.data import DataLoader

# A custom RIA model, optimized for radio classification!
lte_nr_classifier = LTE_NR_CLassifier()

# RIA datasets are compatible with the Torch DataLoader...
train_loader = DataLoader(dataset, batch_size=4)

# ...and can be trained using a PyTorch Lightning Trainer! 
trainer = Trainer()
trainer.fit(model=lte_nr_classifier, train_dataloaders=train_loader)

# Save model as ONNX graph
lte_nr_classifier.to_onnx("models/classifier.onnx")
```

If RIA's syntax feels familiar, that's because RIA is built on [PyTorch](https://pytorch.org/docs/stable/data.html) 
and [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/)!

Please refer to the [documentation](https://radiointelligence.io/) for additional usage examples. If you 
encounter any difficulties, don't hesitate to reach out on our open [support forum](https://github.com/qoherent/ria/discussions/categories/support).

Additional back-ends can be made available. Please [contact us](https://www.qoherent.ai/contact/) for further details.

## 🤝 Contribution

We welcome contributions from the community! Whether it's an enhancement, bug fix, or new how-to guide, your 
input is valuable. To get started, please visit our [Contribution Guidelines](./.github/CONTRIBUTING.md).

If you encounter any issues or to report a security vulnerability, please submit a [bug report](https://github.com/qoherent/ria/issues/new/choose).

If you have a larger project in mind, please [contact us](https://www.qoherent.ai/contact/) directly, we'd love to collaborate with you. 🚀

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](./DIVERSITY_STATEMENT.md).

We kindly insist that all contributors review and adhere to our [Code of Conduct](.github/CODE_OF_CONDUCT.md) and that all code contributors 
review our [Coding Guidelines](.github/CODING.md).

## 🖊️ Authorship

RIA Core is developed and maintained by [Qoherent](https://www.qoherent.ai/), with the invaluable support of 
[many independent contributors](https://github.com/qoherent/ria/graphs/contributors).

If you are doing research with RIA, please cite the project:

> [1] Qoherent Inc., "Radio Intelligence Apps," 2024. [Online]. Available: https://github.com/qoherent/ria

If you like what we're doing, don't forget to give the project a star! ⭐

## 📄 License

RIA Core is **free and open-source**, released under [AGPLv3](https://www.gnu.org/licenses/agpl-3.0.en.html).

Alternative licensing options are available. Please [contact us](https://www.qoherent.ai/contact/) for further details.