D4.1-SotA Review and Initial Definition of BeGREEN O-RAN Intelligent Plane, and AI/ML Algorithms for NFV User-Plane and Edge Service Control Energy Efficiency Optimization
Creators: name: Sánchez González, Juan
name: Pérez-Romero, Jordi
name: Sallent Roig, José Oriol
name: Umbert Juliana, Anna
name: Catalan, Miguel
name: Municio, Esteban
name: Pueyo Morillo, Jorge
name: Tomas, Pau
name: Castellanos, German
name: Berozashvili, Revaz
name: Pryor, Simon
name: Salvat Lozano, Josep Xavier
name: Ayala-Romero, Jose A.
name: Zanzi, Lanfranco
name: Armstrong, Joss
name: Gutiérrez Terán, Jesús
name: Ghoraishi, Mir
Publisher: name: Zenodo
Year: 2023

description: BeGREEN is proposing an evolved Radio Access Network (RAN) for Beyond 5G (B5G) communication networks with the aim of accommodating increased traffic and service demands and of improving energy efficiency. BeGREEN covers a wide range of mechanisms to reduce energy consumption at hardware, link, and system levels.

This deliverable presents an exhaustive SotA review focusing on relevant specifications, developments and projects related to RAN Intelligent Controllers (RIC) and their utilisation for the implementation of intelligent and automated control loops. After this, open-source, commercial and simulated/emulated implementations are described. This document presents the design principles of a BeGREEN O-RAN (Open-RAN) “Intelligent Plane” that allows to introduce Artificial Intelligence (AI) and Machine Learning (ML) control and management plane functions to reduce the overall energy consumption of the RAN infrastructure. It also covers the relationship of the BeGREEN Intelligent Plane with the rest of BeGREEN components and the O-RAN architecture, extending the previous work done in BeGREEN D2.1.

The proposed framework enables the development of new AI/ML procedures that recognise time-space patterns in the system (e.g. evolution of traffic, UE –User Equipment- mobility, etc.) and learn appropriate network configuration or reconfiguration actions to improve the network performance and improve energy efficiency. A description of the state of the art, design principles and an initial design of the proposed AI/ML-assisted procedures is provided. In particular, the proposed solutions cover the use of advanced AI/ML methodologies based on Explainable AI (XAI) that allow the identification of entities and areas of the network where energy savings are achievable and, consequently, improve the energy efficiency. The use of AI/ML algorithms is also proposed to dynamically dimension and allocate the computing resources needed for each vBS (virtual Base Station) in an O-RAN O-Cloud Computing Platform, again to improve the network performance and the energy efficiency. Other proposed AI/ML solutions aim to have an intelligent control of the Radio Unit (RU), Reconfigurable Intelligent Surfaces (RIS) and Relays according to the RAN status and traffic and UE mobility predictions with the aim of reducing the energy consumption and improving the network performance. Moreover, other AI/ML solutions aim to enhance the energy efficiency of edge services hosting User Plane Functions (UPF) Network Functions (NF) by properly tuning the CPU (Central Processing Unit) frequency of the edge server. Finally, a joint orchestration of vRANs (virtual RAN) and Edge AI services is proposed to minimize the overall power consumption subject to the performance constraints of the service. 

This deliverable serves as the reference document to BeGREEN D4.2, where an initial implementation and evaluation of the BeGREEN Intelligent Plane and proposed AI/ML solutions will be presented.