Characterizing 5G User Throughput via Uncertainty Modeling and Crowdsourced Measurements
This work addresses the problem of accurately estimating end-to-end throughput for network operators and users, offering incremental improvements by extending existing methods to 5G with uncertainty modeling.
The paper tackles the challenge of characterizing user throughput in 5G networks by proposing an uncertainty-aware and explainable approach using crowdsourced measurements, achieving an 8.7% improvement in R^2 for 4G methods and providing the first benchmarks for 5G datasets.
Characterizing application-layer user throughput in next-generation networks is increasingly challenging as the higher capacity of the 5G Radio Access Network (RAN) shifts connectivity bottlenecks towards deeper parts of the network. Traditional methods, such as drive tests and operator equipment counters, are costly, limited, or fail to capture end-to-end (E2E) Quality of Service (QoS) and its variability. In this work, we leverage large-scale crowdsourced measurements-including E2E, radio, contextual and network deployment features collected by the user equipment (UE)-to propose an uncertainty-aware and explainable approach for downlink user throughput estimation. We first validate prior 4G methods, improving R^2 by 8.7%, and then extend them to 5G NSA and 5G SA, providing the first benchmarks for 5G crowdsourced datasets. To address the variability of throughput, we apply NGBoost, a model that outputs both point estimates and calibrated confidence intervals, representing its first use in the field of computer communications. Finally, we use the proposed model to analyze the evolution from 4G to 5G SA, and show that throughput bottlenecks move from the RAN to transport and service layers, as seen by E2E metrics gaining importance over radio-related features.