Τίτλος:
Supporting Intelligence in Disaggregated Open Radio Access Networks: Architectural Principles, AI/ML Workflow, and Use Cases
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
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.
Συγγραφείς:
Giannopoulos, A.
Spantideas, S.
Kapsalis, N.
Gkonis, P.
Sarakis, L.
Capsalis, C.
Vecchio, M.
Trakadas, P.
Εκδότης:
Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Λέξεις-κλειδιά:
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
DOI:
10.1109/ACCESS.2022.3166160
