  In this work, 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. The
conventional positioning algorithms will be used as back-up for the
environments with high variability in conditions; but ML-assisted positioning
serves as more efficient and simpler technique to provide better or similar
positioning accuracy. In this regard, we study ML-assisted positioning methods
and evaluate their performance using system level simulations for an outdoor
scenario in Lincoln park Chicago. The study is based on the use of raytracing
tools, a 3GPP 5G NR compliant system level simulator and DL framework to
estimate positioning accuracy of the UE. The use of raytracing tool and system
level simulator helps avoid expensive drive test measurements in practical
scenarios. Our proposed mechanism is a first step towards more proactive
mobility management in future networks. We evaluate and compare performance of
various DL models and show mean positioning error in the range of 1-1.5m for
the best DL configuration with appropriate system feature-modeling.
