ML-Assisted UE Positioning: Performance Analysis and 5G Architecture Enhancements
M. Majid Butt, Anna Pantelidou, Istv\'an Z. Kov\'acs

TL;DR
This paper analyzes the performance of deep learning-based user equipment positioning in 5G networks, highlighting architectural enhancements needed for integrating ML solutions and demonstrating positioning accuracy improvements through simulations.
Contribution
It provides a comprehensive performance analysis of ML-assisted UE positioning in 5G, compares various DL models, and discusses architectural changes for future network integration.
Findings
Mean positioning error of 1-1.5 meters with 2-hidden layer DL models
ML-assisted positioning reduces feedback overhead compared to traditional algorithms
Evaluation based on system-level simulations with raytracing and 3GPP 5G NR standards
Abstract
Artificial intelligence and data-driven networks will be integral part of 6G systems. In this article, we comprehensively discuss implementation challenges and need for architectural changes in 5G radio access networks for integrating machine learning (ML) solutions. As an example use case, we investigate user equipment (UE) positioning assisted by deep learning (DL) in 5G and beyond networks. As compared to state of the art positioning algorithms used in today's networks, radio signal fingerprinting and machine learning (ML) assisted positioning requires smaller additional feedback overhead; and the positioning estimates are made directly inside the radio access network (RAN), thereby assisting in radio resource management. In this regard, we study ML-assisted positioning methods and evaluate their performance using system level simulations for an outdoor scenario. The study is based…
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