Paper: Tingwei Chen, Yantao Wang, Hanzhi Chen, Zijian Zhao, Xinhao Li, Nicola Piovesan, Guangxu Zhu*, Qingjiang Shi, "Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need", IEEE Globecom GenAI NGN Workshop 2025
Runner Up Solution in AI/ML for 5G-Energy Consumption Modelling by ITU AI/ML in 5G Challenge
ITU-AI-ML-in-5G-Challenge/5G-Energy-Consumption-Modelling-CAKE-Team-Solution
Notice: The original data used in our paper cannot be publicly accessed due to copyright restrictions. Therefore, we implemented our method on a similar task instead.
The dataset is sourced from the HKUST COMP 5212 course project (Deed - Attribution-NonCommercial-ShareAlike 4.0 International - Creative Commons). A brief course report is provided for your reference.
To execute the program, please use the following command:
pythonmain.py--arl--norm@INPROCEEDINGS{11590940,
author={Chen, Tingwei and Wang, Yantao and Chen, Hanzhi and Zhao, Zijian and Li, Xinhao and Piovesan, Nicola and Zhu, Guangxu and Shi, Qingjiang},
booktitle={2025 IEEE Globecom Workshops (GC Wkshps)}, title={Modelling the 5G Energy Consumption Using Real-world Data: Energy Fingerprint is All You Need}, year={2025},
volume={},
number={},
pages={1675-1680},
keywords={Modeling;Energy consumption;Training;5G mobile communication;Encoding;Base stations;Printing;Equations;Indexes;Indexing;5G;Base Station;Energy Consumption;Deep Learning},
doi={10.1109/GCWkshps68340.2025.11590940}}