# ML Approach for Power Consumption Prediction in Virtualized Base Stations

arXiv: https://arxiv.org/abs/2301.05764 · HTML: https://mlsanity.com/paper/2301.05764/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 13 Jan 2023.

## Builds on (most cited first, 5 of 5)

- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [srsLTE: An Open-Source Platform for LTE Evolution and Experimentation](https://mlsanity.com/paper/1602.04629/) (2016)
- [Intelligent O-RAN for Beyond 5G and 6G Wireless Networks](https://mlsanity.com/paper/2005.08374/) (2020)
- [A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead](https://mlsanity.com/paper/1604.00786/) (2016)
- [The Complexity-Rate Tradeoff of Centralized Radio Access Networks](https://mlsanity.com/paper/1503.08585/) (2015)

## Built on by (most cited first, 2 of 2)

- [ML-based handover prediction over a real O-RAN deployment using RAN Intelligent controller](https://mlsanity.com/paper/2404.19671/) (2024)
- [ML-based Predictive Models for Power Consumption in Virtualised O-RANs](https://mlsanity.com/paper/2607.24256/) (2026)

## Abstract (arXiv)

The flexibility introduced with the Open Radio Access Network (O-RAN) architecture allows us to think beyond static configurations in all parts of the network. This paper addresses the issue related to predicting the power consumption of different radio schedulers, and the potential offered by O-RAN to collect data, train models, and deploy policies to control the power consumption. We propose a black-box (Neural Network) model to learn the power consumption function. We compare our approach with a known hand-crafted solution based on domain knowledge. Our solution reaches similar performance without any previous knowledge of the application and provides more flexibility in scenarios where the system behavior is not well understood or the domain knowledge is not available.