AI-Enhanced Spatial Spectrum Demand Prediction with Contextual Clustering and Error Correction
Bibliographic record
Abstract
A key capability for future artificial intelligence (AI)-enabled cognitive radio systems is the ability to identify dynamic patterns of spectrum usage and pinpoint areas of under and oversupply of spectrum. For mobile broadband services, the dynamic usage patterns are largely governed by user demand in both time and location. The ability to accurately predict spectrum demand, particularly in dense urban areas, is an essential capability for future intelligent wireless systems. Data-driven methodologies, including the application of machine learning (ML) and advanced analytics, are seen as promising approaches to addressing the complex problem of spectrum demand prediction. However, conventional machine learning models struggle to provide robust and accurate predictions due to the spatially dependent nature of spectrum usage. This paper presents an AI-driven framework for spectrum demand estimation that addresses the spatial dependencies in demand data to improve the accuracy of the predictive models. The approach combines two-stage spatial and feature-based clustering to create representative datasets that capture both spatial and land-use variations. Additionally, a Spatial Error Model (SEM) is integrated to correct for spatial dependencies in the residuals, ensuring robust and unbiased spectrum demand predictions. Experimental results using data from five Canadian cities demonstrate how this framework can enhance spectrum demand predictions and provide insights into usage patterns that can be leveraged by future adaptive cognitive systems.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".