These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
ITU-T Y.3175 - Functional architecture of machine learning-based quality of service assurance for the IMT-2020 network
This Recommendation specifies a functional architecture of quality of service (QoS) assurance based on machine learning (ML) for the international mobile telecommunications-2020 (IMT-2020) network.
This Recommendation includes:
- an overview of the architectural framework for ML in the IMT-2020 network [ITU-T Y.3172];
- the functional architecture of ML-based QoS assurance for the IMT-2020 network;
- reference points of ML-based QoS assurance for the IMT-2020 network;
- procedures of ML-based QoS assurance for the IMT-2020 network.
This Recommendation uses ML only in the context of QoS assurance decision making. Therefore any other use of ML lies outside the scope of this Recommendation © ITU 2022 All rights reserved
The information about this standard has been compiled by the AI Standards Hub, an initiative dedicated to knowledge sharing, capacity building, research, and international collaboration in the field of AI standards. You can find more information and interactive community features related to this standard by visiting the Hub’s AI standards database here. To access the standard directly, please visit the developing organisation’s website.
About the tool
You can click on the links to see the associated tools
Developing organisation(s):
Tool type(s):
Objective(s):
Target sector(s):
Type of approach:
Maturity:
Usage rights:
Geographical scope:
Tags:
- System architecture
Use Cases
Would you like to submit a use case for this tool?
If you have used this tool, we would love to know more about your experience.
Add use case