Iasa Minnesota IT Architect Community

Machine learning (ML) and AI will play a key role in the development of 6G networks. Network virtualization and network software solutions in 5G networks can support data-driven intelligent and automated networks to some extent and this trend will grow in 5G-advanced networks. In 6G networks, network intelligence is envisioned to be end-to-end, and air interface is envisioned to be AI-native. The user equipment (UE) devices need to be smarter, environment and context aware, and capable of running ML algorithms. The mobile networks standardization bodies including 3GPP have started discussing AI solutions in almost all working groups. This talk will give a brief overview of various activities in 3GPP in this domain with focus on the main challenges in developing machine learning solutions in 5G use cases and emphasize with a case study how deployment of these solutions is much harder in a real network as compared to theoretical performance evaluation. Further, a vision for paradigm shift
from AI-as-an-enabler to AI-Native air-interface will be provided for 6G networks.