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

TL;DR

This paper develops a neural network-based model to accurately estimate the power consumption of 5G multi-carrier base stations, aiding energy efficiency analysis and design.

Contribution

It introduces a novel neural network model for predicting 5G AAU power consumption, capturing complex behaviors and demonstrating generalization across different architectures.

Findings

Model achieves high estimation accuracy.

Neural network captures energy savings benefits.

Model scalable with training data size.

Abstract

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.