  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.
