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.
@article{arxiv.2301.05764,
title = {ML Approach for Power Consumption Prediction in Virtualized Base Stations},
author = {Merim Dzaferagic and Jose A. Ayala-Romero and Marco Ruffini},
journal= {arXiv preprint arXiv:2301.05764},
year = {2023}
}