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The fifth generation of the Radio Access Network (RAN) has brought newservices, technologies, and paradigms with the corresponding societal benefits.However, the energy consumption of 5G networks is today a concern. In recentyears, the design of new methods for decreasing the RAN power consumption hasattracted interest from both the research community and standardization bodies,and many energy savings solutions have been proposed. However, there is still aneed to understand the power consumption behavior of state-ofthe-art basestation architectures, such as multi-carrier active antenna units (AAUs), aswell as the impact of different network parameters. In this paper, we present apower consumption model for 5G AAUs based on artificial neural networks. Wedemonstrate that this model achieves good estimation performance, and it isable to capture the benefits of energy saving when dealing with the complexityof multi-carrier base stations architectures. Importantly, multiple experimentsare carried out to show the advantage of designing a general model able tocapture the power consumption behaviors of different types of AAUs. Finally, weprovide an analysis of the model scalability and the training datarequirements.

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