Enhancing 5G NSA Performance with Lightweight Network Data Analytics Loops

Abstract

The progress of 5G Non-Standalone (NSA) networks is getting slow because of the different nature of the deployments where the management of mobility, beam selection, and scheduling have a direct impact on performance. This paper builds on the previous NSA benchmarking and introduces a lightweight NWDAF-like loop that uses real-time field key performance indicators (KPIs) to optimize network behavior as an interactive loop. By doing so, the system in question is able to vary handover thresholds, beam selection, and scheduling parameters thereby allowing the closed-loop, KPI-driven optimization without the need for a fully deployed 5G standalone core. The field tests conducted in urban-rural mixed scenarios showed that there were improvements that could be measured in terms of latency, throughput, and mobility efficiency thereby demonstrating the practical advantages of protoNWDAF implementations. Moreover, this method of approach not only helps in the future smart network optimization but also provides a scalable framework for connecting the traditional NSA deployments and future 5G core networks. Hence, the outcome proves that even the slightest of analytics loops have the capability to drastically improve the performance of NSA networks thus opening the door for more complex NWDAF-enabled 5G operations.