Energy Efficiency in Open RAN: RF Channel Reconfiguration Use Case

Abstract

Recently, energy efficiency (EE) has been pointed out as one of the key requirements within mobile networks. The development of intelligent algorithms providing Radio Access Networks (RAN) with EE features is possible when having access to the Key Performance Indicators (KPIs), and proper control actions, e.g., cell on/off switching, or Radio Frequency (RF) channel reconfiguration. These features together with a Machine Learning (ML) framework are available in the O-RAN architecture. This paper provides an overview of the EE framework according to the use cases specified by the O-RAN ALLIANCE. It is followed by the implementation of the Energy Saving rApp (ES-rApp). The rApp utilizes Deep Q-Learning (DQL) to increase EE through intelligent RF channel reconfiguration. Simulation results show up to a 24.8% EE gain over the static RF Channel Configuration (RCC).

Keywords

• Open RAN
• 5G
• energy efficiency
• RAN intelligent controller
• deep Q-learning
• massive MIMO