V2X-mmWave-Beamforming

A project on deep learning based multi-modality sensing in 60 GHz mmWave beamforming for connected vehicles.

Paper:

The codes are implemented for the following paper:

M.B. Mollah, H. Wang, M.A. Karim, H. Fang, "Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learning", IEEE Transactions on Cognitive Communications and Networking, vol. 12, pp. 327-341, 2026.

M.B. Mollah, H. Wang, H. Fang, "Position Aware 60 GHz mmWave Beamforming for V2V Communications Utilizing Deep Learning", ICC 2024 - IEEE International Conference on Communications, Denver, CO, USA, 2024, pp. 4711-4716.

Citatation:

@article{mollah2025multi-modality,
  title={Multi-Modality Sensing in mm{W}ave Beamforming for Connected Vehicles Using Deep Learning},
  author={Mollah, Muhammad Baqer and Wang, Honggang and Karim, Mohammad Ataul and Fang, Hua},
  journal={IEEE Transactions on Cognitive Communications and Networking},
  year={2025},
  volume={},
  number={},
  pages={1-15}
}
@inproceedings{mollah2024position,
  title={Position Aware 60 {GH}z mm{W}ave Beamforming for {V}2{V} Communications Utilizing Deep Learning}, 
  author={Mollah, Muhammad Baqer and Wang, Honggang and Fang, Hua},
  booktitle={ICC 2024 - IEEE International Conference on Communications},
  pages={4711-4716},
  year={2024},
  organization={IEEE}
}

Paper: https://arxiv.org/pdf/2509.11112

Datasets: https://www.deepsense6g.net/scenarios/

Feel free to conctact: m.m.baqer@ieee.org