TY - JOUR
TI - Supporting Intelligence in Disaggregated Open Radio Access Networks: Architectural Principles, AI/ML Workflow, and Use Cases
AU - Giannopoulos, A.
AU - Spantideas, S.
AU - Kapsalis, N.
AU - Gkonis, P.
AU - Sarakis, L.
AU - Capsalis, C.
AU - Vecchio, M.
AU - Trakadas, P.
JO - IEEE Access
PY - 2022
VL - 10
TODO - null
SP - 39580-39595
PB - Institute of Electrical and Electronics Engineers, Inc. (IEEE)
SN - 2169-3536
TODO - 10.1109/ACCESS.2022.3166160
TODO - Computer architecture;  Deep learning;  Distributed computer systems;  Energy efficiency;  Interactive computer systems;  Network architecture;  Radio;  Radio access networks;  Radio communication;  Real time systems;  Reinforcement learning;  Resource allocation;  Supervised learning, Artificial intelligence learning;  B5G;  Cloud-computing;  Intelligent controllers;  Machine-learning;  Open radio access network;  Radio access networks;  Radio intelligent controller;  Real - Time system;  Reinforcement learnings;  Resource management;  Resources allocation;  Software;  Supervised learning, 5G mobile communication systems
TODO - Driven by the emerging trend for transparent, open and programmable communications, Open Radio Access Network (O-RAN) constitutes the dominant architectural approach for deploying the future wireless networks. Towards standardizing and specifying the building blocks and principles of O-RAN, a coordinated global effort has been observed, mainly comprised of the O-RAN Alliance, the operators and several research activities. This paper presents the architectural aspects and the current status of O-RAN deployments, integrating both existing and ongoing activities from the O-RAN enablers. Furthermore, since the Artificial Intelligence and Machine Learning (AI/ML) act as key pillars for realizing O-RANs, a comprehensive view on the AI/ML functionality is provided as well. Additionally, a Network Telemetry (NT) architecture is also proposed to ensure end-to-end data collection and real-time analytics. To concretely illustrate the O-RAN supporting mechanisms for hosting AI/ML, we implemented two realistic ML algorithms: (i) a Supervised Learning (SL) based algorithm for cell traffic prediction using the training data of an open dataset and (ii) a Deep Reinforcement Learning (DRL) based algorithm for energy-efficiency maximization using a 5G-compliant simulator to obtain RAN measurements. We schematically demonstrate the AI/ML workflow for both ML-assisted algorithms through the usage of xApps running on the Radio Intelligent Controller (RIC), as well as we outline the role of the O-RAN components involved in the AI/ML loop. Combining the high-level architectural descriptions with a detailed presentation of ML-empowered resource allocation schemes, the paper discusses and summarizes the O-RAN disaggregation principles and the role of AI/ML embedded in future O-RAN deployments. © 2013 IEEE.
ER -