Publication Details
AI-driven intent-based networking for 5G enhanced robot autonomy
Lessi Christina, M.Sc.
Xu Zhao
Špaňhel Jakub, Ing., Ph.D. (DCGM)
Qiu Renxi, Dr.
Lendinez Adrian, BSc
Chondroulis Ioannis
Belikaidis Ioannis, Ph.D.
5G, Intent-based networking, Enhanced robot autonomy, 5G-ERA, Machine Learning,
Semantic models, autonomous robots
Innovative 5G orchestration architectures so far, have been mainly designed and
optimized for Quality of Service (QoS), but are not aware of Quality of
Experience (QoE). This makes intent recognition and End-to-End interpretability
an inherited problem for orchestration systems, leading to possible creation of
ineffective control policies. In this paper, an intent-based networking for
autonomous robots is being proposed and demonstrated through the 5G-ERA project.
In particular, to map an intent from individual vertical action to a global OSM
control policy, a workflow of four tools is proposed: i) Action Sequence
Generation, ii) Network Intent Estimation, iii) Resource Usage Forecasting, and
iv) OSM Control Policy Generation. All of these tools are described in the paper
with specific function descriptions, inputs, outputs and the semantic
models/Machine Learning tools that have been used. Finally, the paper presents
the developed intent-based dashboard for the visualization of the tools outputs,
whilst taking QoE into consideration.
@inproceedings{BUT182277,
author="Marios {Sophocleous} and Christina {Lessi} and Zhao {Xu} and Jakub {Špaňhel} and Renxi {Qiu} and Adrian {Lendinez} and Ioannis {Chondroulis} and Ioannis {Belikaidis}",
title="AI-driven intent-based networking for 5G enhanced robot autonomy",
booktitle="AIAI 2022 IFIP WG 12.5 International Workshops",
year="2022",
series="IFIP Advances in Information and Communication Technology",
journal="IFIP Advances in Information and Communication Technology",
volume="652",
number="2022",
pages="61--70",
publisher="Springer Nature Switzerland AG",
address="Cham",
doi="10.1007/978-3-031-08341-9\{_}6",
issn="1868-422X"
}