https://github.com/malrabeiah/Sub6-Preds-mmWave

Using sub-6 GHz channels to predict mmWave beams and link blockage.
https://github.com/malrabeiah/Sub6-Preds-mmWave

5g channel-mapping deep-learning matlab mmwave

Last synced: 3 months ago
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Using sub-6 GHz channels to predict mmWave beams and link blockage.

• Host: GitHub
• URL: https://github.com/malrabeiah/Sub6-Preds-mmWave
• Owner: malrabeiah
• License: other
• Created: 2019-05-14T22:29:27.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-12-06T03:01:04.000Z (almost 3 years ago)
• Last Synced: 2024-04-08T07:33:22.424Z (7 months ago)
• Topics: 5g, channel-mapping, deep-learning, matlab, mmwave
• Language: MATLAB
• Homepage:
• Size: 29.3 KB
• Stars: 31
• Watchers: 3
• Forks: 18
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

Awesome Lists containing this project

• awesome-5g - Sub-6 Predicts mmWave Beam-forming Vectors - Using sub-6 GHz channels to predict mmWave beams and link blockage. (Research / Diameter)

README

        
# Sub-6 Predicts mmWave Beam-forming Vectors:

This is an implementation of the Deep Learning (DL) solution that uses sub-6 GHz channels to predict top-n beams of mmWave users. With the approperiate modifications and given the right dataset, it could also be used to generate all the figures in [Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels](https://ieeexplore.ieee.org/document/9121328).

# Requirements:

Essential:

1- MATLAB deep learning toolbox.

2- [DeepMIMO dataset](https://deepmimo.net/)

Optional:

1- NVIDIA GPU card.

2- CUDA toolkit.

3- cuDNN package.

# Running Instructions:

1- Generate the datasets using scenarios [O1_28](https://deepmimo.net/scenarios/o1-scenario/) and [O1_3p5](https://deepmimo.net/scenarios/o1-scenario/) in the DeepMIMO dataset. Use the parameters listed in Table.1, Section VII-B of [the paper](https://ieeexplore.ieee.org/document/9121328).

2- Prepare two MATLAB structures, one for sub-6GHz data and the other for 28GHz. Please refer to the comments at the beginning of main.m for more information on the data structures.

3- Assign the paths to the two MATLAB structures to the two parameters: options.dataFile1 and options.dataFile2 in the beginning of main.m.

4- Run main.m to get the figure 4-b in the paper.

REMARK: Transmit power range is defined in tx_power in main.m.

# Citation:

If you use these codes or a modified version of them, please cite the following work:

```

@ARTICLE{Alrabeiah2020,

  author={Alrabeiah, Muhammad and Alkhateeb, Ahmed},

  journal={IEEE Transactions on Communications}, 

  title={Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6 GHz Channels}, 

  year={2020},

  volume={68},

  number={9},

  pages={5504-5518},

  doi={10.1109/TCOMM.2020.3003670}}

```

# License:

This code package is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/)