# Machine Learning and Analytical Power Consumption Models for 5G Base Stations

arXiv: https://arxiv.org/abs/2209.11600 · HTML: https://mlsanity.com/paper/2209.11600/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 23 Sep 2022.

## Builds on (most cited first, 2 of 2)

- [A Survey on 5G Radio Access Network Energy Efficiency: Massive MIMO, Lean Carrier Design, Sleep Modes, and Machine Learning](https://mlsanity.com/paper/2101.11246/) (2021)
- [Optimal Design of Energy-Efficient Multi-User MIMO Systems: Is Massive MIMO the Answer?](https://mlsanity.com/paper/1403.6150/) (2014)

## Built on by (most cited first, 7 of 7)

- [Large Language Models for Telecom: Forthcoming Impact on the Industry](https://mlsanity.com/paper/2308.06013/) (2023)
- [Power Consumption Modeling of 5G Multi-Carrier Base Stations: A Machine Learning Approach](https://mlsanity.com/paper/2212.04318/) (2022)
- [Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need](https://mlsanity.com/paper/2406.16929/) (2024)
- [Data-driven Energy Efficiency Modelling in Large-scale Networks: An Expert Knowledge and ML-based Approach](https://mlsanity.com/paper/2401.00443/) (2023)
- [On the Impact of PRB Load Uncertainty Forecasting for Sustainable Open RAN](https://mlsanity.com/paper/2407.14400/) (2024)
- [A Multi-Armed Bandit Framework for Online Optimisation in Green Integrated Terrestrial and Non-Terrestrial Networks](https://mlsanity.com/paper/2506.09268/) (2025)
- [A Peer-Relative Representation Learning Framework for Energy Inefficiency Identification in Mobile Network Sites](https://mlsanity.com/paper/2609.03809/) (2026)

## Abstract (arXiv)

The energy consumption of the fifth generation(5G) of mobile networks is one of the major concerns of the telecom industry. However, there is not currently an accurate and tractable approach to evaluate 5G base stations (BSs) power consumption. In this article, we propose a novel model for a realistic characterisation of the power consumption of 5G multi-carrier BSs, which builds on a large data collection campaign. At first, we define a machine learning architecture that allows modelling multiple 5G BS products. Then, we exploit the knowledge gathered by this framework to derive a realistic and analytically tractable power consumption model, which can help driving both theoretical analyses as well as feature standardisation, development and optimisation frameworks. Notably, we demonstrate that such model has high precision, and it is able of capturing the benefits of energy saving mechanisms. We believe this analytical model represents a fundamental tool for understanding 5G BSs power consumption, and accurately optimising the network energy efficiency.