MIMO4BERT-AI4Wireless is a comprehensive repository dedicated to the implementation, evaluation, and analysis of the MIMO4BERT model, a novel transformer-based approach aimed at enhancing wireless communication systems.
The MIMO4BERT model leverages transformer architectures to improve the reconstruction and prediction of Channel State Information (CSI) in dynamic wireless environments. This repository provides the necessary code, datasets, and documentation to facilitate research and development in this area.
- Implementation: Complete codebase for the MIMO4BERT model, including data preprocessing, model training, and evaluation scripts.
- Datasets: Tools and instructions for generating and processing wireless CSI datasets.
- Experiments: Detailed setups for experiments assessing reconstruction accuracy, robustness, and interpretability across various scenarios.
- Visualization: Scripts for visualizing attention mechanisms and analyzing model performance.
- Documentation: Comprehensive explanations of the methodology, system design, and experimental results.
To get started with MIMO4BERT-AI4Wireless, follow these steps:
-
Clone the Repository:
git clone https://github.com/ocatak/MIMO4BERT-AI4Wireless.git
-
Install Dependencies: Navigate to the project directory and install the required dependencies:
cd MIMO4BERT-AI4Wireless pip install -r requirements.txt -
Generate Datasets: Use the provided scripts to generate and preprocess the necessary datasets. Refer to the
data/README.mdfor detailed instructions.
@misc{catak2025bert4mimofoundationmodelusing,
title={BERT4MIMO: A Foundation Model using BERT Architecture for Massive MIMO Channel State Information Prediction},
author={Ferhat Ozgur Catak and Murat Kuzlu and Umit Cali},
year={2025},
eprint={2501.01802},
archivePrefix={arXiv},
primaryClass={cs.IT},
url={https://arxiv.org/abs/2501.01802},
}
This project is licensed under the MIT License. See the LICENSE file for details.