Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need
This work addresses energy efficiency for 5G network operators, representing an incremental improvement with specific gains in modeling accuracy.
The paper tackled the problem of accurately modeling 5G base station energy consumption to address sustainability challenges, proposing a deep learning model that integrates BSID embeddings and attention mechanisms, resulting in a reduction of MAPE from 12.75% to 4.98%, a 60% performance gain.
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.