The group will define the requirements of Machine Learning as they relate to technology, data formats and network architectures.

A new ITU Focus Group will propose standardization strategies to assist Machine Learning in contributing to the efficiency of emerging IMT-2020 (5G) systems. The group will define the requirements of Machine Learning as they relate to technology, data formats and network architectures. Contributions to the first meeting of the Focus Group in Geneva, 30 January to 2 February 2018, discussed 5G systems' significant gains in complexity over 4G systems, highlighting potential for Machine Learning to make reliable predictions; support robust, efficient network operations; and increase the feasibility of network self-organization. WG'Use cases, services & requirements' will document promising use cases of Machine Learning and describe their surrounding technological ecosystem. The study will identify the requirements of these use cases and bring greater clarity to the ICT industry's vision for Machine Learning's contribution to 5G systems.

The International Telecommunications Union (ITU), the United Nations specialised agency for information and communication technologies (ICTs), has launched a new ITU Focus Group to establish a basis for ITU standardisation to assist machine learning (ML) in bringing more automation and intelligence to ICT network design and management. Machine learning algorithms are helping operators to make smarter use of network-generated data. These algorithms enable ICT networks and their components to adapt their behaviour autonomously in the interests of efficiency, security and optimal user experience. Fixed and mobile networks generate a huge amount of data both at the network infrastructure level and at the user/customer level, which contain a lot of useful information such as data on location, mobility and call patterns. New ML methods for big data analytics in communication networks can extract relevant information from the network data, and then leverage this knowledge for autonomic network control and management as well as service provisioning.