| Project Description: We are witnessing a new green transformation of communication networks, which is driven by the European green agenda. In terms of cellular networks, one of the driving forces enabling intelligent control is the O-RAN architecture (Open Radio Access Networks), which allows to dynamically optimize the network performance. O-RAN makes it possible to use open source tools to collect important data from various parts of the network and to adjust the network configuration accordingly. The O-RAN architecture also envisions control entities that can take advantage of machine learning algorithms that can predict future network states based on the historical measurements. Goal: The goal of this project is to study, carrying out experimental tests, how different policies and configurations in the radio access network affect the energy consumption, and to explore multiple ways to minimize the energy consumption. Machine learning approaches will be used to estimate and predict energy usage under different operating conditions. |
School of Computer Science and Statistics