Measurement-Driven Modeling of End-to-End Latency in an Indoor Private Standalone 5G Network

Abstract

This paper presents a measurement-driven study of end-to-end latency in an indoor private standalone 5G network deployed at TU Wien IFT TEC-Lab. The testbed combines pico radio units, edge computing resources, and a local 5G core to form a campus-scale private network architecture designed for low-latency communication. To characterize network performance, TCP throughput and UDP one-way latency measurements were collected in a single-user, constant-bit-rate downlink setting at seven predefined indoor locations using iPerf-based tests at six traffic levels (1–500 Mbps), with five repetitions per condition. Based on these measurements, a compact parametric model was calibrated for this deployment to describe latency as a function of achieved bandwidth and location-dependent effects. The results show that latency remained low and relatively stable at low and medium traffic levels, generally staying below 20 ms between 1 and 200 Mbps, but increased more strongly as the operating point approached the practical throughput limit of the setup. The fitted model captured the overall latency trend with an in-sample MAE of 2.52 ms, an RMSE of 3.13 ms, and an R² of 0.842, while retaining comparable predictive performance under a trial-based test split (R² = 0.835) and leave-one-location-out validation (R² = 0.818). The compact model also achieved lower out-of-sample errors than the minimal M/M/1-type and polynomial-regression baselines under both validation schemes. Overall, the findings indicate that traffic load was the main driver of latency growth in the studied environment, while spatial effects remained measurable but secondary. The resulting formulation should be understood as an interpretable empirical model of end-to-end latency for one indoor private standalone 5G deployment under single-user, constant-bit-rate downlink conditions, rather than as a general latency model for private 5G networks. Within that scope, the model provides an interpretable description of the measured latency behavior and a basis for preliminary capacity assessment in this deployment. Its applicability to another private 5G network has not been established and would require a new measurement campaign, complete parameter re-estimation, and independent validation.