https://github.com/unifyair/handover-prediction
A state-of-the-art deep learning framework for predicting user mobility patterns in 5G networks, enabling proactive handover optimization.
https://github.com/unifyair/handover-prediction
5g
Last synced: 3 months ago
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A state-of-the-art deep learning framework for predicting user mobility patterns in 5G networks, enabling proactive handover optimization.
- Host: GitHub
- URL: https://github.com/unifyair/handover-prediction
- Owner: UnifyAir
- Created: 2025-04-18T22:36:02.000Z (6 months ago)
- Default Branch: master
- Last Pushed: 2025-04-22T10:02:43.000Z (6 months ago)
- Last Synced: 2025-05-17T00:14:02.103Z (5 months ago)
- Topics: 5g
- Language: Jupyter Notebook
- Homepage: https://unifyair.io
- Size: 709 KB
- Stars: 4
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🚀 5G Handover Prediction
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
[](https://github.com/unifyair/handover-prediction)
[](https://unifyair.io/docs)
[](https://huggingface.co/unifyair)
⚡ A state-of-the-art deep learning framework for predicting user mobility patterns in 5G networks, enabling proactive handover optimization.
## 🌟 Features
- **Advanced LSTM Models**: Leveraging state-of-the-art sequence modeling for accurate mobility prediction
- **Real-time Processing**: Optimized for low-latency predictions in production environments
- **Scalable Architecture**: Built with cloud-native principles for easy deployment
- **Comprehensive Evaluation**: Extensive metrics and visualization tools for model assessment
## 🔗 Resources
- **Model**: [UnifyAir Handover Prediction Model](https://huggingface.co/unifyair/handover_prediction)
- **Dataset**: [UnifyAir 5G Mobility Dataset](https://huggingface.co/datasets/unifyair/mobility_data)
## 📁 Project Structure
```
mobility-prediction-5g/
│
├── README.md # Project overview and documentation
├── LICENSE # MIT License
├── .gitignore # Git ignore rules
├── requirements.txt # Python dependencies
├── setup.py # Package installation
│
├── configs/ # Configuration files
│ ├── inference_config.yaml # Inference settings
│ ├── training_config.yaml # Training parameters
│ ├── generation_config.yaml # Data generation config
│ └── optimization_config.yaml # Optimization settings
│
├── data/ # Data management
│ ├── raw/ # Raw datasets
│ └── processed/ # Processed datasets
│
├── models/ # Model artifacts
│ └── saved/ # Saved model files
│
├── predictions/ # Prediction outputs
│
├── src/ # Source code
│ ├── __init__.py # Package initialization
│ ├── preprocessing.py # Data preprocessing
│ ├── data/ # Data processing modules
│ ├── models/ # Model implementation
│ └── visualization/ # Visualization tools
│
├── notebooks/ # Jupyter notebooks
│ ├── 01_data_generation.ipynb # Data generation notebook
│ ├── 02_data_exploration.ipynb # Data exploration notebook
│ ├── 03_model_training.ipynb # Model training notebook
│ └── 04_inference_demo.ipynb # Inference demonstration notebook
│
└── scripts/ # Command-line tools
```
## 🚀 Getting Started
1. **Clone the repository**
```bash
git clone https://github.com/unifyair/handover-prediction.git
cd handover-prediction
```
2. **Set up the environment**
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
```
3. **Prepare your data**
```bash
# Place your raw mobility data in data/raw/
# The data should be in CSV format with columns: timestamp, user_id, location_id, signal_strength
```
4. **Configure training parameters**
```bash
# Edit configs/training_config.yaml to set your training parameters:
# - batch_size
# - learning_rate
# - num_epochs
# - sequence_length
# - hidden_size
```
5. **Train the model**
```bash
python scripts/train.py --config configs/training_config.yaml
```
6. **Make predictions**
```bash
python scripts/predict.py --model_path models/saved/your_model.pt --input data/raw/test_data.csv
```
For detailed information about the model architecture and usage, please refer to our [Hugging Face Model Page](https://huggingface.co/unifyair/handover_prediction).
## 📊 Results
Our models achieve state-of-the-art performance on mobility prediction tasks:
- **Accuracy**: 94.5% on test set
- **Latency**: < 10ms inference time
- **Memory**: < 500MB model size
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.
## 📝 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 📫 Contact
- **Project Link**: [https://github.com/unifyair/mobility-prediction-5g](https://github.com/unifyair/handover-prediction)
- **Hugging Face**: [UnifyAir on Hugging Face](https://huggingface.co/unifyair)
- **Website**: [UnifyAir](https://unifyair.io)
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Built with ❤️ by the UnifyAir Team