Modeling Local Demand for Mobile Spectrum using Large Crowdsourced Datasets
Bibliographic record
Abstract
With the deployment and expansion of 5G networks underway in many countries, the demand for mobile spectrum continues to grow, particularly in frequency bands below 6 GHz. The emergence of 6G networks will also further amplify current challenges associated with spectrum scarcity. This paper discusses the need to model mobile spectrum demand - defined in terms of the demand for mobile services - at the local level. More accurate spectrum demand modeling can help regulatory bodies to better plan spectrum allocations and to make more informed spectrum policy decisions to support future technological developments. While existing research typically estimates demand using simplistic models based on factors such as network capacity, spectral efficiency, and population-based proxies, this work proposes a data-driven approach to estimate mobile spectrum demand using machine learning. We also derive a more accurate proxy for demand using a large dataset of over 2.5 billion crowdsourced commercial mobile measurements. The data-driven nature of this proxy eliminates the need for various theoretical assumptions associated with current demand proxies. Finally, we employ the SHapley Additive exPlanations (SHAP) method for global model interpretation to demonstrate that, contrary to intuition, population is not the sole contributing factor of demand. Instead, a diverse set of real-world factors can influence demand patterns and, therefore, should be used to create more accurate demand models.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".