🚀 5G Handover Prediction

⚡ 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
• Dataset: UnifyAir 5G Mobility Dataset

📁 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

   git clone https://github.com/unifyair/handover-prediction.git
   cd handover-prediction

2. Set up the environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt

3. Prepare your data

   # 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

   # Edit configs/training_config.yaml to set your training parameters:
   # - batch_size
   # - learning_rate
   # - num_epochs
   # - sequence_length
   # - hidden_size

5. Train the model

   python scripts/train.py --config configs/training_config.yaml

6. Make predictions

   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.

📊 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 for details.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

📫 Contact

• Project Link: https://github.com/unifyair/mobility-prediction-5g
• Hugging Face: UnifyAir on Hugging Face
• Website: UnifyAir

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_{Built with ❤️ by the UnifyAir Team}