https://github.com/gitdata-ga/itu5g
This GitHub repository presents our solution to the International Telecommunication Union (ITU) 5G Energy Consumption Modeling Challenge hosted on Zindi. The challenge addresses concerns over the energy consumption of 5G network deployments, despite being more energy-efficient than 4G networks.
https://github.com/gitdata-ga/itu5g
5g deep-learning lightgbm machine-learning python random-forest tensorflow tree xgboost zindi
Last synced: 2 months ago
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This GitHub repository presents our solution to the International Telecommunication Union (ITU) 5G Energy Consumption Modeling Challenge hosted on Zindi. The challenge addresses concerns over the energy consumption of 5G network deployments, despite being more energy-efficient than 4G networks.
- Host: GitHub
- URL: https://github.com/gitdata-ga/itu5g
- Owner: GitData-GA
- License: other
- Created: 2023-08-28T22:17:24.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-03-04T13:22:57.000Z (over 2 years ago)
- Last Synced: 2024-03-04T22:35:24.167Z (over 2 years ago)
- Topics: 5g, deep-learning, lightgbm, machine-learning, python, random-forest, tensorflow, tree, xgboost, zindi
- Language: Jupyter Notebook
- Homepage: https://itu5g.gd.edu.kg/
- Size: 6.28 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ITU 5G Challenge 2023
[Version 1 (current)](https://itu5g.gd.edu.kg/)
**Last Update: October 24, 2022**
This GitHub repository presents our solution to the International Telecommunication Union (ITU) 5G Energy Consumption Modeling Challenge hosted on Zindi. The challenge addresses concerns over the energy consumption of 5G network deployments, despite being more energy-efficient than 4G networks. The goal is to optimize base station parameters and energy-saving methods for more efficient network deployments.
## Authors
1. Zhilu Chen (
)
2. Rui Hu
3. Hengyuan Liu (
)
4. Li Yuan (
)
