# Power Consumption Modeling of 5G Multi-Carrier Base Stations: A Machine Learning Approach

arXiv: https://arxiv.org/abs/2212.04318 · HTML: https://mlsanity.com/paper/2212.04318/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 8 Dec 2022.

## Builds on (most cited first, 4 of 4)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [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)
- [Machine Learning and Analytical Power Consumption Models for 5G Base Stations](https://mlsanity.com/paper/2209.11600/) (2022)

## Built on by (most cited first, 3 of 3)

- [Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need](https://mlsanity.com/paper/2406.16929/) (2024)
- [On the Impact of PRB Load Uncertainty Forecasting for Sustainable Open RAN](https://mlsanity.com/paper/2407.14400/) (2024)
- [Label Unbalance in High-frequency Trading](https://mlsanity.com/paper/2503.09988/) (2025)

## Abstract (arXiv)

The fifth generation of the Radio Access Network (RAN) has brought new services, technologies, and paradigms with the corresponding societal benefits. However, the energy consumption of 5G networks is today a concern. In recent years, the design of new methods for decreasing the RAN power consumption has attracted interest from both the research community and standardization bodies, and many energy savings solutions have been proposed. However, there is still a need to understand the power consumption behavior of state-ofthe-art base station architectures, such as multi-carrier active antenna units (AAUs), as well as the impact of different network parameters. In this paper, we present a power consumption model for 5G AAUs based on artificial neural networks. We demonstrate that this model achieves good estimation performance, and it is able to capture the benefits of energy saving when dealing with the complexity of multi-carrier base stations architectures. Importantly, multiple experiments are carried out to show the advantage of designing a general model able to capture the power consumption behaviors of different types of AAUs. Finally, we provide an analysis of the model scalability and the training data requirements.