Network slicing in 5G RAN enables building logical networks on top of physical infrastructure. In case of RAN spectrum resources are divided into small bandwidth parts then, the network function in core network, namely Network Slice Admission Control Function (NSACF) enables to limit the count of user equipments (UE) and packet data unit (PDU) sessions in a slice. This paper presents negative results and a practical analysis of how many UEs and PDU sessions can be allowed to use if available bandwidth is fixed and tries to evaluate the optimal count of subslices at this bandwidth. In case of slice resource overutilization there are 2 options: increase resources or move UEs to another slice. In this paper, the feasibility of the other option is evaluated in case of fixed overall bandwidth where resource increase is not possible. Results show that infrastructure can afford as few slices as possible and slicing does not help if theoretical capacity is exceeded.
In this paper we evaluate the performance of fifth generation new radio (5G NR) based positioning under realistic conditions model for cooperative connected automated mobility (CCAM) scenarios. We benchmark the performance using 3GPP release 16 proposed new positioning reference signal (PRS), of positioning in 5G NR mobile networks. Time difference of arrival (TDoA) positioning methods is one of the widely used method which is used for localization. Simulation results are showing that under line-of-sight (LOS) conditions, the desired positioning accuracy is achievable for various CCAM use-cases. In best case scenarios precision is less than 1m in 80% of cases. In more realistic cases, when there is no line of sight (NLOS) between user terminal (UT) and network nodes, then accuracy decreases significantly. Methods of automatic classification of LOS/NLOS channels are thus needed. TDoA positioning method suffers degradation of performance, when different network nodes are out of sync with each other. Thus, other methods, less sensitive to synchronization error, such as round-trip time (RTT) or angle-based measurements might be worth considering.
In 5G and beyond, the network slicing is a crucial feature that ensures the fulfillment of service requirements. Nevertheless, the impact of the number of slices and slice size on the radio access network (RAN) slice performance has not yet been studied. This research is needed to understand the effects of creating subslices on slice resources to serve slice users and how the performance of RAN slices is affected by the number and size of these subslices. A slice is divided into numbers of subslices of different sizes, and the slice performance is evaluated based on the slice bandwidth utilization and slice goodput. A proposed subslicing algorithm is compared with k-means UE clustering and equal UE grouping. The MATLAB simulation results show that subslicing can improve slice performance. If the slice contains all UEs with a good block error ratio (BLER), then a slice performance improvement of up to 37% can be achieved, and it comes more from the decrease in bandwidth utilization than the increase in goodput. If a slice contains UEs with a poor BLER, then the slice performance can be improved by up to 84%, and it comes only from the goodput increase. The most important criterion in subslicing is the minimum subslice size in terms of resource blocks (RB), which is 73 for a slice that contains all good-BLER UEs. If a slice contains UEs with poor BLER, then the subslice can be smaller.
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