Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912583927201792 |
|---|---|
| author | Chen, Tingwei Wang, Yantao Chen, Hanzhi Zhao, Zijian Li, Xinhao Piovesan, Nicola Zhu, Guangxu Shi, Qingjiang |
| author_facet | Chen, Tingwei Wang, Yantao Chen, Hanzhi Zhao, Zijian Li, Xinhao Piovesan, Nicola Zhu, Guangxu Shi, Qingjiang |
| contents | The introduction of 5G technology has revolutionized communications, enabling unprecedented capacity, connectivity, and ultra-fast, reliable communications. However, this leap has led to a substantial increase in energy consumption, presenting a critical challenge for network sustainability. Accurate energy consumption modeling is essential for developing energy-efficient strategies, enabling operators to optimize resource utilization while maintaining network performance. To address this, we propose a novel deep learning model for 5G base station energy consumption estimation based on a real-world dataset. Unlike existing methods, our approach integrates the Base Station Identifier (BSID) as an input feature through an embedding layer, capturing unique energy patterns across different base stations. We further introduce a masked training method and an attention mechanism to enhance generalization and accuracy. Experimental results show significant improvements, reducing Mean Absolute Percentage Error (MAPE) from 12.75% to 4.98%, achieving over 60% performance gain compared to existing models. The source code for our model is available at https://github.com/RS2002/ARL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16929 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need Chen, Tingwei Wang, Yantao Chen, Hanzhi Zhao, Zijian Li, Xinhao Piovesan, Nicola Zhu, Guangxu Shi, Qingjiang Signal Processing Artificial Intelligence The introduction of 5G technology has revolutionized communications, enabling unprecedented capacity, connectivity, and ultra-fast, reliable communications. However, this leap has led to a substantial increase in energy consumption, presenting a critical challenge for network sustainability. Accurate energy consumption modeling is essential for developing energy-efficient strategies, enabling operators to optimize resource utilization while maintaining network performance. To address this, we propose a novel deep learning model for 5G base station energy consumption estimation based on a real-world dataset. Unlike existing methods, our approach integrates the Base Station Identifier (BSID) as an input feature through an embedding layer, capturing unique energy patterns across different base stations. We further introduce a masked training method and an attention mechanism to enhance generalization and accuracy. Experimental results show significant improvements, reducing Mean Absolute Percentage Error (MAPE) from 12.75% to 4.98%, achieving over 60% performance gain compared to existing models. The source code for our model is available at https://github.com/RS2002/ARL. |
| title | Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2406.16929 |