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 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 on the use of raytracing tool, a 3GPP 5G NR compliant system level simulator and DL framework to estimate positioning accuracy of the UE. We evaluate and compare performance of various DL models and show mean positioning error in the range of 1-1.5m for a 2-hidden layer DL architecture with appropriate feature-modeling. Building on our performance analysis, we discuss pros and cons of various architectures to implement ML solutions for future networks and draw conclusions on the most suitable architecture.

On the Scheduling and Power Control for Uplink Cellular-Connected UAV Communications

Cellular connected unmanned aerial vehicle (UAV) has been identified as a promising paradigm and attracted a surge of research interest recently. Although the nearly line-of-sight (LoS) channels are favorable to receive higher powers, UAV can in turn cause severe interference to each other and to any other users in the same frequency band. In this contribution, we focus on the uplink communications of cellular-connected UAV. To cope with the severe interference among UAV-UEs, several different scheduling and power control algorithms are proposed to optimize the spectrum efficiency (SE) based on the geometrical programming (GP) principle together with the successive convex approximation (SCA) technique. The proposed schemes include maximizing the sum SE of UAVs, maximizing the minimum SE of UAVs, etc., applied in the frequency domain and/or the time domain. Moreover, the quality of service (QoS) constraint and the uplink single-carrier (SC) constraint are also considered. The performances of these power and resource allocation algorithms are evaluated via extensive simulations in both full buffer transmission mode and bursty traffic mode. Numerical results show that the proposed algorithms can effectively enhance the uplink SEs of cellular-connected UAVs.