Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need

• URL: http://arxiv.org/abs/2406.16929v2
• Date: Fri, 12 Sep 2025 07:09:41 GMT
• Title: Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need
• Authors: Tingwei Chen, Yantao Wang, Hanzhi Chen, Zijian Zhao, Xinhao Li, Nicola Piovesan, Guangxu Zhu, Qingjiang Shi,
• Abstract summary: 5G technology has led to a substantial increase in energy consumption, presenting a critical challenge for network sustainability.<n>We propose a novel deep learning model for 5G base station energy consumption estimation based on a real-world dataset.<n> Experimental results show significant improvements, reducing Mean Absolute Percentage Error (MAPE) from 12.75% to 4.98%.
• Score: 35.242972942661275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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