Energy Consumption of Machine Learning Enhanced Open RAN: A Comprehensive Review

Abstract

The Open Radio Access Network (RAN) emerges as a revolutionary architecture promising unprecedented levels of openness, flexibility, and intelligence within radio access networks. Central to this innovation is the integration of Machine Learning (ML) and Artificial Intelligence (AI) within the RAN Intelligent Controller (RIC), aimed at optimizing network operations and enhancing control mechanisms. This paper undertakes a thorough examination of Open RAN, particularly focusing on its energy consumption aspects, which are pivotal for ensuring the sustainability of future wireless networks. In this paper, we review and compare Open RAN architecture with previous network architectures. In particular we focus on O-RAN Alliance specifications. Additionally, we explore the deployment of ML across various facets of Open RAN and highlights how to estimate the energy consumption of ML models. Through constructing explicit energy consumption models for key O-RAN components, we provide a granular analysis of their energy profiles. Finally we compare the energy dynamics of O-RAN against traditional RAN architectures, delineating the impact of virtualization and disaggregation on energy efficiency.

Keywords

• Open radio access network (Open RAN)
• energy efficiency
• machine learning
• disaggregation