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.

通过使用射线追踪工具和系统级模拟器，研究使用深度学习技术辅助用户设备在 5G 及更高网络中的定位能力，这项技术在无线资源管理方面更为高效和简单，并为未来网络提供了更积极的移动管理机制。

5G 下的 RF 指纹和深度学习辅助 UE 定位

RF Fingerprinting and Deep Learning Assisted UE Positioning in 5G

Integrated Access and Backhaul (IAB) is being investigated as a means to
overcome deployment costs of ultra-dense 5G millimeter wave (mmWave) networks
by realizing wireless backhaul links to relay the access traffic. For the
development of these systems, however, it is fundamental to validate the
performance of IAB in realistic scenarios through end-to-end system level
simulations. In this paper, we shed light on the most recent standardization
activities on IAB, and compare architectures with and without IAB in mmWave
deployments. While it is well understood that IAB networks reduce deployment
costs by obviating the need to provide wired backhaul to each cellular
base-station, in this paper we demonstrate the cell-edge throughput advantage
offered by IAB using end-to-end system level simulations. We further highlight
some research challenges associated with this architecture that will require
further investigations.

通过系统级模拟验证了无线远程振翅 (IAB) 在 5G 毫米波 (millimeter wave) 网络中的性能，并展示了 IAB 具有的边缘吞吐量优势；此外，还讨论了与该体系结构相关的一些研究挑战。