MassiveMIMO-CSI-Dataset

A comprehensive Massive MIMO Channel State Information (CSI) dataset designed for next-generation wireless communication research and artificial intelligence/machine learning (AI/ML) applications. This dataset is a valuable resource for researchers in 5G/6G networks, Massive MIMO, and data-driven AI methodologies.

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Key Features

• Realistic Scenarios: Includes stationary, high-speed mobility, and urban macrocell environments based on 3GPP standards.
• Versatility: Designed for applications such as scenario classification, anomaly detection, and CSI prediction using machine learning and deep learning.
• Reproducibility: Accompanied by a MATLAB script to generate the dataset and Jupyter Notebook examples to explore its use cases.

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Why Use This Dataset?

This dataset bridges the gap between AI/ML research and wireless communication by providing:

1. Comprehensive Data: Incorporates realistic channel profiles, user mobility models, and noise conditions.
2. Benchmarking: Ideal for benchmarking ML/DL models in Massive MIMO research.
3. Wide Applicability: Supports research in areas such as:
   • Channel estimation and prediction.
   • Intelligent reflecting surfaces (IRS).
   • Spectrum sensing and beamforming.

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Repository Contents

1. foundation_model_data_generator.m: MATLAB script to generate the Massive MIMO CSI dataset.
2. examples/ Directory: Jupyter Notebook examples demonstrating dataset use cases, such as:
   • Scenario classification (e.g., stationary vs. high-speed mobility).
   • Anomaly detection using Autoencoders and Isolation Forests.
   • CSI prediction with regression models.

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Access the Dataset

You can either:

1. Generate the Dataset: Use the MATLAB script included in this repository to generate your own dataset.
2. Download the Pre-Generated Dataset: The pre-generated dataset is available on Zenodo for direct download:
   • Massive MIMO CSI Dataset on Zenodo (Add the actual link once available)

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How to Use

1. Generate the Dataset

• Install MATLAB with the 5G Toolbox.
• Run the provided script foundation_model_data_generator.m.
• The generated .mat file will be saved locally.

2. Analyze the Dataset

• Open the Jupyter Notebook examples under the examples/ directory.
• Use Python libraries to visualize and analyze the dataset. Example tasks include:
  • Scenario classification.
  • Anomaly detection.
  • CSI magnitude prediction.

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Target Audience

This dataset is particularly useful for:

• Wireless Communication Researchers working on Massive MIMO, 5G/6G networks, and smart wireless systems.
• AI/ML Practitioners interested in applying machine learning to real-world wireless datasets.

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Keywords

• Massive MIMO CSI Dataset
• 5G and 6G wireless networks
• Channel State Information for AI/ML
• Wireless communication datasets
• Machine learning in wireless systems
• Next-generation networks dataset

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Dependencies

To use the Jupyter Notebook examples:

• Required libraries: numpy, matplotlib, scipy, pandas, seaborn, scikit-learn, tensorflow.
• Install dependencies with:

pip install -r requirements.txt

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License

This project is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). You are free to use, modify, and share this dataset with proper attribution.

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How to Cite

If you use this dataset in your research, please cite:

Ferhat Ozgur Catak. "Massive MIMO CSI Dataset for Next-Generation Networks Research." Zenodo, 2024.

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Contact

For questions or collaborations, contact:

• Name: Ferhat Ozgur Catak
• Email: [email protected]
• Institution: University of Stavanger

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Call to Action

Explore the dataset today and accelerate your research in Massive MIMO, 5G/6G networks, and AI/ML-driven wireless communication solutions!