https://github.com/muhd-umer/comyx

Comyx is an optimized and modular Python library for simulating wireless communication systems
https://github.com/muhd-umer/comyx

6g noma numba numpy pytorch ray reinforcement-learning ris scipy simulation star-ris wireless-communication

Last synced: 19 days ago
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Comyx is an optimized and modular Python library for simulating wireless communication systems

• Host: GitHub
• URL: https://github.com/muhd-umer/comyx
• Owner: muhd-umer
• License: mit
• Created: 2023-05-11T15:04:18.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-11-08T20:26:39.000Z (6 months ago)
• Last Synced: 2025-04-08T12:49:33.054Z (22 days ago)
• Topics: 6g, noma, numba, numpy, pytorch, ray, reinforcement-learning, ris, scipy, simulation, star-ris, wireless-communication
• Language: Python
• Homepage: https://comyx.readthedocs.io/
• Size: 47.6 MB
• Stars: 15
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Codeowners: .github/CODEOWNERS

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**Comyx** is a Python library for simulating wireless communication systems. It uses **NumPy** and **SciPy** for numerical computation, and **Numba** for just-in-time (JIT) compilation. It provides a number of features for simulating wireless communication systems:

- **B5G Features**: Supports a variety of B5G specific features, such as STAR-RIS, and NOMA.

- **Channel Models**: Provides the AWGN, Rayleigh, and Rician fading models.

- **Signal Modulation**: Supports a variety of modulation schemes, such as BPSK, QPSK, and QAM.

- **Performance Metrics**: Can calculate a variety of performance metrics, such as the sum rate, and outage probability.

## To-Do

- [ ] Update documentation

- [ ] Add network optimization support

- [ ] Add Reinforcement Learning (RL) support

## Installation

You can install the latest version of the package using pip:

```shell

pip install comyx

```

*Note: It is recommended to create a new virtual environment so that updates/downgrades of packages do not break other projects.*

Or you can clone the repository along with research code and perform an editable installation:

```shell

git clone https://github.com/muhd-umer/comyx.git

pip install -e .

```

**Reinforcement Learning (RL) Support**

For RL support, you will need to install the following dependencies:

- Install PyTorch (Stable)

    ```shell

    pip install torch torchvision torchaudio

    ```

- Install Ray RLlib

    ```shell

    pip install -U ray[default]  # core, dashboard, cluster launcher

    pip install -U ray[rllib]  # tune, rllib

    ```