A Spatiotemporal and User-Centric Assessment of 5G Enhanced Mobile Broadband Service in Addis Ababa
Bibliographic record
Abstract
The telecom industry is globally moving toward 5G network to address high bandwidth, low latency, and massive connectivity requirements of innovative digital services. The deployment of 5G technology in Addis Ababa, Ethiopia is expected to address the speed and quality requirements of mobile broadband services in the business districts and selected residential areas of the city. However, practically achieved spatial and temporal end-user quality of service and experience from this 5G network has not been studied. This paper presents a spatiotemporal and user-centric assessment of quality of service and quality of experience for 5G enhanced mobile broadband service in Addis Ababa using objective and subjective methods. Measurement tools including a crowdsourcing mobile application (Ookla SpeedTest), a network management system (Huawei iMaster performance reporting system), and a drive test tool (probe handset unit) were applied alongside subjective surveys. The results reveal that, despite the significant disparity in its coverage, Addis Ababa’s 5G network performs above global and ITU IMT-2020 benchmarks for both download and upload throughput, with notable variances based on device type and spatiotemporal factors. Findings of this research work provide stakeholders with early-stage performance benchmarks to guide future enhancements and optimizations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".