DeepSpectrum: A Localized Demand Estimation Model for Mobile Spectrum using Deep Learning
Bibliographic record
Abstract
With the emergence of many new 5G and 6G use cases, the demand for spectrum continues to grow. In response, spectrum regulators worldwide are actively exploring innovative approaches to manage spectrum more efficiently. Spectrum sharing emerges as a particularly promising approach, as it allows for more intensive use of spectrum by enabling other services and users to access idle bands. Nevertheless, identifying areas where spectrum is either under- or over-supplied poses a significant challenge for regulators, given that demand insights are typically only observable to mobile operators.This paper proposes DeepSpectrum, a novel Deep Learning (DL) model that employs Multi-Task Learning, to estimate the local demand for mobile spectrum. The proposed model is trained on publicly available geospatial datasets and is used to estimate a novel demand proxy derived from crowdsourced data. The model features a combination of Convolutional Neural Networks and custom Residual Networks to extract relevant patterns from the data itself, thereby eliminating the need for traditional manual feature engineering. Overall, DeepSpectrum achieves a performance improvement of over 20% compared to alternative ML regression algorithms, demonstrating the advantage of DL for more accurate spectrum demand modeling.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".