ITU-T Y Supplement 55
ITU-T Y.3170-series – Machine learning in future networks including IMT-2020: use cases
This Supplement analyses use cases for machine learning in future networks including IMT-2020, and presents them in a unified format.…
Telecom and network-based services
ITU-T Y Supplement 70
ITU-T Y.3800-series – Quantum key distribution networks – Applications of machine learning
This Supplement presents the applications of machine learning (ML) in quantum key distribution networks (QKDNs). In particular, this Supplement includes:…
Recommendation ITU-T P.1402 introduces machine-learning techniques and their application for quality of service (QoS) and quality of experience (QoE) prediction…
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…
This Recommendation specifies a functional framework for network service provisioning based on artificial intelligence (AI) in future networks, including international…
This Recommendation provides a framework for artificial intelligence (AI) enhanced telecom operation and management (AITOM). It describes the functional framework…
This Recommendation1 specifies a framework in the form of constraints, performance criteria and methods for the development of intrusive parametric,…
Recommendation ITU-T Y.3173 specifies a framework for evaluating the intelligence of future networks including IMT-2020 and a method for evaluating…
ITU-T Y.3174
Framework for data handling to enable machine learning in future networks including IMT-2020
This Recommendation provides a framework for data handling to enable machine learning (ML) in future networks including International Mobile Telecommunications…
ETSI GR ENI 004 V 1.1.1
Experiential Networked Intelligence (ENI) – Terminology for Main Concepts in ENI
The WI will provide terms and definitions used within the scope of the ISG ENI, in order to achieve a…
ETSI GR ENI 018 V 2.1.1
Experiential Networked Intelligence (ENI) – Introduction to Artificial Intelligence Mechanisms for Modular Systems
The purpose of the present document is to provide information on different types of AI mechanisms that can be used…
ETSI GS ENI 005 V 1.1.1
Experiential Networked Intelligence (ENI) – System Architecture
The purpose of this work item is to draft a GS to define the software functional architecture of ENI. This…
