Towards Efficient Spectrum Management: A Data-driven Assessment Framework for Local Licensing
Bibliographic record
Abstract
The emerging demand for localized private networks, tailored to specific and diverse use cases, has triggered growing attention to the development of local spectrum licensing approaches that depart from traditional schemes used for broad coverage. In particular, there is a significant need for forward-looking quantitative studies to inform technical choices and ensure that licensing conditions are aligned with overarching goals, such as supporting high spectrum re-use across potentially dense network deployments. Addressing a gap in the current research literature, we introduce a novel data-driven framework to assess the potential effectiveness of local spectrum licensing schemes from a regulatory viewpoint. This approach leverages real-world data to emulate prospective local deployment scenarios, capturing important geographic details such as high-demand market areas, realistic industry locations, and high-resolution clutter information. We discuss the practical application of the framework to a case study focused on the 3.9 GHz band in Canada. Using this case study, we demonstrate the merits of the approach, highlighting the importance of incorporating contextually relevant geospatial datasets to better inform local licensing regulations.
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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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".