Supporting Intelligence in Disaggregated Open Radio Access Networks: Architectural Principles, AI/ML Workflow, and Use Cases

IEEE Access

Panagiotis Gkonis¹  Lambros Sarakis¹  Sotirios Spantideas²  Nikolaos Kapsalis²  Panagiotis Trakadas²  Anastasios Giannopoulos²  Massimo Vecchio³  Christos Capsalis⁴ 

[1] Department of Digital Industry Technologies, National and Kapodistrian University of Athens, Euboea, Greece;Department of Ports Management and Shipping, National and Kapodistrian University of Athens, Euboea, Greece;Fondazione Bruno Kessler, Trento, Italy;School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;

关键词: 5G;    B5G;    O-RAN;    AI/ML;    radio intelligent controller;    resource allocation;   

DOI  :  10.1109/ACCESS.2022.3166160

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown