article · Computer Science and Information Technologies
Cellular data speeds fluctuate significantly across different locations and operational settings because of signal interference, propagation effects, and congestion. To address this, a hierarchical mixed-effects modelling framework combined with Weibull reliability analysis was developed to assess spatiotemporal throughput variation in operational cellular environments. The evaluation utilised over 77,000 field measurements gathered across university campus locations in Ghana, examining metrics such as reference signal received power, reference signal received quality, and round-trip time. The findings revealed that all measured factors significantly influence throughput, with signal quality acting as the primary driver alongside strong spatial differences. Fixed predictors accounted for 18.9 percent of performance variation, rising to 45.1 percent when spatial effects were incorporated. Additionally, reliability assessments showed that the probability of sustaining speeds of 5 Mbps and 10 Mbps stood at 39.7 percent and 22.5 percent, respectively.
Mobile users depend on stable internet connections, yet cellular speeds vary unpredictably across different areas and conditions. By accurately mapping how physical location and signal quality combine to affect network speeds, this approach helps mobile operators diagnose the root causes of poor service, supporting better planning and more consistent mobile broadband performance for consumers.
This framework offers a practical analytical tool for mobile network operators, infrastructure planners, and telecommunications regulators conducting reliability-aware network optimisation and performance evaluation. Validated on over 77,000 real-world field measurements, the work sits at an applied and tested stage, potentially enabling network providers to benchmark quality of service and guide targeted engineering investments.
AI-generated from the published abstract. Always read the original work before citing.
Reliable cellular throughput is essential for ensuring consistent user experience in modern mobile networks, yet it exhibits significant variability across spatial and operational conditions due to propagation effects, interference, and network congestion. This study proposes a hierarchical mixed-effects modeling framework integrated with Weibull-based reliability analysis to characterize spatiotemporal throughput variability in real-world operating conditions. The analysis is based on a large-scale dataset comprising over 77,000 field measurements collected across multiple university campus locations in Ghana, enabling cross-layer evaluation of network performance using key indicators, including reference signal received power (RSRP), reference signal received quality (RSRQ), and round-trip time (RTT). Analysis results indicate that all modeled predictors significantly influence throughput performance, with signal quality emerging as the dominant factor, alongside notable spatial heterogeneity. The model explains 18.9% of variability using fixed effects and 45.1% when spatial effects are included. Reliability analysis indicates that the probability of achieving 5 Mbps and 10 Mbps is 39.7% and 22.5%, respectively. These findings demonstrate that the proposed framework effectively captures throughput variability, spatial heterogeneity, and probabilistic service reliability in operational cellular environments, providing a practical analytical framework for reliability-aware cellular network optimization and performance evaluation.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.11591/csit.v7i3.p241-255
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
