INTEGRATING MACHINE LEARNING WITH OPEN-SOURCE 5G SA TESTBEDS FOR PERFORMANCE ANALYSIS AND KPI TIME SERIES MODELING

Abstract

Open source 5G Standalone (SA) testbeds provide cost-effective environments for research and teaching, yet most existing implementations focus primarily on functional validation rather than leveraging machine learning for advanced network analytics. This study presents a comprehensive framework integrating SARIMAX, LSTM, and Transformer models with a fully operational 5G SA testbed combining Open5GS, srsRAN, MongoDB, and ZeroMQ-based RF emulation. The primary objective is to demonstrate predictive analytics capabilities for 5G network performance forecasting using real testbed-generated Key Performance Indicator (KPI) data. A comparative forecasting analysis was conducted using the three models trained on KPI datasets augmented through CTGAN synthetic data generation. Experimental validation confirmed reliable end-to-end 5G operation with synchronized configuration across PLMN, TAC, DNN, and security parameters. Under controlled single-UE, RF-free conditions, the testbed achieved ultra-low latency (1.34 ms RTT), near-gigabit throughput (847 Mbps downlink, 823 Mbps uplink), and rapid PDU session establishment (0.22 s). Performance profiling identified the User Plane Function (UPF) and database interactions as primary scaling bottlenecks. The machine learning evaluation revealed that while SARIMAX provides a reliable statistical baseline, neural network models achieve substantially higher forecasting accuracy for network KPIs. These results demonstrate the extensibility of open source 5G testbeds toward intelligent network management and predictive analytics applications.