Supporting Intelligence in Disaggregated Open Radio Access Networks: Architectural Principles, AI/ML Workflow, and Use Cases-Reference-Cited by-同舟云学术

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

Published:2022 Issue: Volume:10 Page:39580-39595
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Giannopoulos Anastasios¹^ORCID,Spantideas Sotirios¹,Kapsalis Nikolaos¹,Gkonis Panagiotis²^ORCID,Sarakis Lambros²^ORCID,Capsalis Christos³,Vecchio Massimo⁴^ORCID,Trakadas Panagiotis¹^ORCID

Affiliation:

1. Department of Ports Management and Shipping, National and Kapodistrian University of Athens, Euboea, Greece

2. Department of Digital Industry Technologies, National and Kapodistrian University of Athens, Euboea, Greece

3. School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

4. Fondazione Bruno Kessler, Trento, Italy

Funder

European Commission through the Affordable5G Project

Horizon 2020 and 5G-PPP Programs

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09754560.pdf?arnumber=9754560

Reference37 articles.

1. AI and ML—Enablers for beyond 5G networks;kaloxylos,2020

2. Power Control in 5G Heterogeneous Cells Considering User Demands Using Deep Reinforcement Learning

3. Joint Energy-efficient and Throughput-sufficient Transmissions in 5G Cells with Deep Q-Learning

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