Recommendation ITU-T P.1402 introduces machine-learning techniques and their application for quality of service (QoS) and quality of experience (QoE) prediction…
Telecom and network-based services
ETSI TS 128 105
5G – Management and orchestration – Artificial Intelligence/ Machine Learning (AI/ML) management (3GPP TS 28.105 version 17.0.0 Release 17)
ITU-T M.3382
Requirements for work order processing in telecom management with artificial intelligence
Recommendation ITU-T M.3382 provides the requirements for work order processing in telecom management with artificial intelligence (AI). Based on AI…
ITU-T Y.3180
Mechanism of traffic awareness for application-descriptor-agnostic traffic based on machine learning
Recommendation ITU-T Y.3180 specifies the mechanism of traffic awareness for application_x0002_descriptor-agnostic traffic based on machine learning. This Recommendation specifies the…
Recommendation ITU-T Y.3654 specifies the mechanisms of machine learning in big data driven networking (bDDN). A set of related aspects…
ITU-T F.748.12
Deep learning software framework evaluation methodology
A deep learning software framework provides an easy and fast way for manufactures to develop their own artificial intelligence (AI)…
ITU-T M.3381
Requirements for energy saving management of 5G radio access network (RAN) systems with artificial intelligence (AI)
Recommendation ITU-T M.3381 provides requirements for energy saving management of a 5G radio access network (RAN) system with artificial intelligence…
Recommendation ITU-T P.565 provides the output of the framework which is a machine learning based speech quality prediction model that…
ETSI GR ENI 012 V 1.1.1
Experiential Networked Intelligence (ENI) – Reactive In-situ Flow Information Telemetry
The present document will describe the motivation, requirements, and challenges of using flow-oriented on-path telemetry techniques which provides relevant measurement…
ETSI TR 103748 V 1.1.1
Core Network and Interoperability Testing (INT) – Artificial Intelligence (AI) in Test Systems and Testing of AI Models – Use and Benefits of AI Technologies in Testing
The present document provides guidance on the use of AI technologies applied to testing. © Copyright 2023, ETSI
ETSI GS ENI 006 V 2.1.1
Experiential Networked Intelligence (ENI) – Proof of Concepts Framework
This Work Item specifies a framework for use within ETSI ENI ISG to coordinate and promote public demonstrations of Proof…
