ML-Assisted UE Positioning: Performance Analysis and 5G Architecture Enhancements

Artificial intelligence and data-driven networks will be integral part of 6G systems. In this article, we comprehensively discuss implementation challenges need for architectural changes in 5G radio access integrating machine learning solutions. As an example use case, investigate user equipment positioning assisted by deep (DL) beyond networks. compared to state the art algorithms used today's networks, signal fingerprinting (ML) requires smaller additional feedback overhead; estimates are made directly inside network (RAN), thereby assisting resource management. regard, study ML-assisted methods evaluate their performance using system level simulations outdoor scenario. The is based on raytracing tool, a 3GPP NR compliant simulator DL framework estimate accuracy UE. We compare various models show mean error range 1-1.5m 2-hidden layer architecture with appropriate feature-modeling. Building our analysis, pros cons architectures implement ML solutions future draw conclusions most suitable architecture.