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

TL;DR

This paper presents a deep learning model that accurately estimates 5G base station energy consumption using real-world data, incorporating unique station identifiers and advanced techniques to significantly improve prediction accuracy and support sustainable network operations.

Contribution

The paper introduces a novel deep learning approach that includes BSID embedding, masked training, and attention mechanisms for precise 5G energy consumption modeling.

Findings

Reduced MAPE from 12.75% to 4.98%.

Achieved over 60% performance improvement.

Demonstrated effectiveness of incorporating BSID in energy prediction.

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.