Data-Driven Modelling of Mobile Network Demand for Efficient Spectrum Management
Bibliographic record
Abstract
As research in wireless communications shifts towards developing the next generation of mobile networks, the demand for spectrum — specifically, the demand for mobile services — continues to grow. Many 6G verticals are envisioned to have much higher data and capacity requirements, which, in turn, will increase the need for more spectrum. While current demand within a mobile network might be known to its respective operator, it is not easily observable by spectrum regulators. Understanding current demand and identifying potential factors that drive demand can greatly assist regulators in ensuring that spectrum is managed and released efficiently to best support emerging technologies and use cases.In this paper, we leverage a large variety of input features derived from publicly available geospatial datasets in conjunction with a gradient boosting tree-based machine learning model to estimate current demand for mobile services at a local level. The proposed model is able to capture over 60% of the variance in the data for two different test scenarios, effectively outperforming three different baseline algorithms. We also employ a gain-based feature importance algorithm to identify potential key drivers of spectrum demand, which contrast the simple intuition that demand is driven only by population or economic activity. Finally, we build a more sparse and interpretable model to help regulators make more informed spectrum planning decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".