Context-Aware Location Management of Groups of Devices in 5G Networks (original) (raw)
Related papers
Mobile Device Localization in 5G Wireless Networks
Workshop on Computing, Networking and Communications , 2019
As wireless networks are evolving into 5G, tremendous amount of data will be shared on the newly developed open source platforms. These data can be used in developing new services. Among which, location information of mobile devices are extremely useful. For example, the location information can be used to assist wireless operators to trouble shoot the network performance. It can also be used to provide some location assisted service. However, some of these devices may be designed for limited budget that do not have the capability of GPS. Furthermore , operators may not have access to the GPS information on the mobile devices. In this paper, we propose a novel machine learning based approach to estimate the location of the mobile devices based on the measurement data that mobiles reported during every call and session. Our proposed algorithm utilizes the advanced features of 5G wireless network, such as the beam information. Simulation shows that the proposed solution can achieve 4m accuracy for LoS enviorment and 12m accuracy for mixed LoS and NLoS environment. And the proposed algorithm can also work even with only the information from one base station.
1st International Conference on Engineering and Applied Natural Sciences, 2022
Improving energy efficiency to extend the lifetime of User Equipment (UE) batteries is among the key performance requirement for the fifth-generation (5G) network goals. To realize this goal, this work proposes a hybrid scheme that mitigates the UE power consumed by the location management procedures in 5G. The approach utilizes a hybrid scheme that embeds a UE Identifier (UEID) partitioning scheme that directional pages UEs into a UE mobility tracking scheme. The approach is resilient to several practical scenarios by configuring a gNB with the UE Access Stratum (AS) context stored between itself and UEs in an inactive state to beamsweep the UEs last registered cell area/RNA before directionally paging the intended UEs. The proposed approach is implemented on a modified network architecture to minimize the power consumption of UEs in both location management procedures at higher frequencies, which is an enabling factor for mm-wave systems. Simulation results of this approach showed an average of 399mW of power consumed in a 24 hour period, which represents a 6% mean percentage reduction in UE power consumption when compared to the existing scheme.