Installation

Backend (C++)

Operating System

• Ubuntu 24.04 LTS
  • Download: http://www.ubuntu.com/download/desktop
• macOS (Apple Silicon, local development / demo only)
  • Guide: Separate macOS build guide

Dependencies & Installation

1. UHD (USRP Hardware Driver)

Install the UHD toolchain by following the official Ettus guide (Please follow the tutorial for Ubuntu 24.04):

• Building and Installing the USRP Open-Source Toolchain on Linux

Note: This code has been tested on UHD v4.9.0.1. You can checkout this version using git checkout v4.9.0.1.

2. Install Aff3ct

This project uses the Aff3ct library for Forward Error Correction (FEC). Install it from source:

sudo apt-get install nlohmann-json3-dev
git clone https://github.com/aff3ct/aff3ct.git
cd aff3ct
git submodule update --init --recursive
mkdir build
cd build
cmake .. -G"Unix Makefiles" -DCMAKE_CXX_COMPILER="g++" -DCMAKE_BUILD_TYPE="Release" -DCMAKE_CXX_FLAGS="-funroll-loops -march=native" -DAFF3CT_COMPILE_EXE="OFF" -DAFF3CT_COMPILE_SHARED_LIB="ON" -DSPU_STACKTRACE="OFF" -DSPU_STACKTRACE_SEGFAULT="OFF" -DCMAKE_CXX_FLAGS="${CMAKE_CXX_FLAGS} -faligned-new"
make -j$(nproc)
sudo make install

3. Clone Repository

Clone the OpenISAC repository:

cd ~
git clone https://github.com/zhouzhiwen2000/OpenISAC.git

4. Configuration

The system uses YAML files for runtime configuration.

• Config filenames: BS reads BS.yaml, and UE reads UE.yaml from the working directory.
• First run: Template YAML files live in config/. Copy config/BS_X310.yaml / config/BS_B210.yaml or config/UE_X310.yaml / config/UE_B210.yaml to BS.yaml / UE.yaml, then edit them in place. For the B210 TDD duplex preset, copy config/BS_B210_Duplex.yaml and config/UE_B210_Duplex.yaml.

Frontend (Python)

It is recommended to use a conda or venv environment with Python 3.13.

Miniconda Installation Guide:

• Windows: Miniconda Installation for Windows
• Linux: Miniconda Installation for Linux

Create a new conda environment:

conda create -n OpenISAC python=3.13
conda activate OpenISAC

Install Dependencies

pip install -r requirements.txt

Note: ffmpeg is required for video streaming demonstrations.

• Ubuntu: sudo apt install ffmpeg
• Windows: Download from ffmpeg.org and add to PATH, or place executable in the working directory.

Enable GPU Acceleration (Optional)

If an Nvidia GPU is available, install cupy-cuda12x to enable GPU acceleration:

Note: Please ensure that the CUDA Toolkit is installed before installing CuPy.

pip install cupy-cuda12x

Enable Intel Integrated GPU Acceleration (Optional)

If your device has an Intel integrated GPU (e.g., Intel UHD Graphics, Intel Iris Xe, etc.), you can enable GPU acceleration via dpctl and dpnp. This is especially useful for laptops and desktops without a dedicated Nvidia GPU.

1. Install Intel GPU Official Driver

First, ensure your system has the latest Intel GPU driver installed:

• Windows: Download and install the latest driver from Intel Download Center: https://www.intel.com/content/www/us/en/download-center/home.html
• Ubuntu: Install Intel compute-runtime:

  sudo apt install intel-opencl-icd libze-intel-gpu1 libze1 intel-media-va-driver-non-free

2. Install Python Dependencies

pip install dpctl dpnp

3. Verify Installation

Run the following command to check if the Intel GPU is correctly recognized:

python -c "import dpctl; print(dpctl.get_devices())"

If the installation is successful, you should see output similar to:

[<dpctl.SyclDevice [backend_type.level_zero, device_type.gpu, Intel(R) UHD Graphics] at 0x...>]

4. Usage Notes

The OpenISAC frontend automatically detects available GPU backends. The priority order is:

1. Nvidia GPU (CUDA)
2. Intel iGPU (dpnp)
3. CPU (fallback)

No code modifications are required; the system will automatically select the best available backend.