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Documentation\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/view-docs-blue?style=flat\" /\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n    \u003ca href=\"https://numpy.org/\" alt=\"NumPy\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/NumPy-%23013243.svg?style=flat\u0026logo=numpy\u0026logoColor=white\" /\u003e\u003c/a\u003e\n    \u003ca href=\"https://scipy.org/\" alt=\"SciPy\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/SciPy-%230C55A5.svg?style=flat\u0026logo=scipy\u0026logoColor=white\" /\u003e\u003c/a\u003e\n    \u003ca href=\"https://numba.pydata.org/\" alt=\"Numba\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/Numba-009ed9?style=flat\u0026logo=numba\u0026logoColor=white\" /\u003e\u003c/a\u003e\n    \u003ca href=\"https://pytorch.org/\" alt=\"PyTorch\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?flat\u0026logo=PyTorch\u0026logoColor=white\" /\u003e\u003c/a\u003e\n\u003c/p\u003e --\u003e\n\n\u003cbr/\u003e\n\n[![build](https://github.com/muhd-umer/comyx/workflows/build/badge.svg)](https://github.com/muhd-umer/comyx/actions?query=workflow:\"build\")\n[![GitHub release](https://img.shields.io/github/release/muhd-umer/comyx?include_prereleases=\u0026sort=semver\u0026color=blue)](https://github.com/muhd-umer/comyx/releases/)\n[![License](https://img.shields.io/badge/license-MIT-blue?style=flat)](#license)\n[![view - Documentation](https://img.shields.io/badge/view-docs-blue?style=flat)](https://comyx.readthedocs.io/)\n[![NumPy](https://img.shields.io/badge/NumPy-%23013243.svg?style=flat\u0026logo=numpy\u0026logoColor=white)](https://numpy.org/)\n[![SciPy](https://img.shields.io/badge/SciPy-%230C55A5.svg?style=flat\u0026logo=scipy\u0026logoColor=white)](https://scipy.org/)\n[![Numba](https://img.shields.io/badge/Numba-009ed9?style=flat\u0026logo=numba\u0026logoColor=white)](https://numba.pydata.org/)\n[![PyTorch](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?flat\u0026logo=PyTorch\u0026logoColor=white)](https://pytorch.org/)\n\n**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:\n\n- **B5G Features**: Supports a variety of B5G specific features, such as STAR-RIS, and NOMA.\n- **Channel Models**: Provides the AWGN, Rayleigh, and Rician fading models.\n- **Signal Modulation**: Supports a variety of modulation schemes, such as BPSK, QPSK, and QAM.\n- **Performance Metrics**: Can calculate a variety of performance metrics, such as the sum rate, and outage probability.\n\n## To-Do\n- [ ] Update documentation\n- [ ] Add network optimization support\n- [ ] Add Reinforcement Learning (RL) support\n\n## Installation\n\nYou can install the latest version of the package using pip:\n\n```shell\npip install comyx\n```\n\n*Note: It is recommended to create a new virtual environment so that updates/downgrades of packages do not break other projects.*\n\nOr you can clone the repository along with research code and perform an editable installation:\n\n```shell\ngit clone https://github.com/muhd-umer/comyx.git\npip install -e .\n```\n\n**Reinforcement Learning (RL) Support**\n\nFor RL support, you will need to install the following dependencies:\n\n- Install PyTorch (Stable)\n\n    ```shell\n    pip install torch torchvision torchaudio\n    ```\n\n- Install Ray RLlib\n\n    ```shell\n    pip install -U ray[default]  # core, dashboard, cluster launcher\n    pip install -U ray[rllib]  # tune, rllib\n    ```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmuhd-umer%2Fcomyx","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmuhd-umer%2Fcomyx","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmuhd-umer%2Fcomyx/lists"}