Cooperative Spectrum Sensing as Image Segmentation: A New Data Fusion Scheme
Bibliographic record
Abstract
Reliable and efficient identification of spectrum access opportunities is crucial for cognitive radio ad hoc networks (CRAHNs). A promising method to improve sensing reliability is CSS, where sensing data from multiple secondary users is combined to facilitate decision making. However, the performance of CSS algorithms in CRAHNs is limited by two challenges. First, the spectrum occupancy status in CRAHNs is spatially heterogeneous. Second, malfunctioning nodes or malicious attackers may falsify the true sensing results. To handle both these challenges, inspired by image segmentation in computer vision, we propose a novel graph-cut CSS algorithm. This algorithm is robust in the presence of false sensing data reports, computationally efficient (with polynomial worst case time complexity), and can be implemented distributively. Theoretically, it can be regarded as a new data fusion scheme that is generalized from classical likelihood ratio tests when sensing information is fully exploited, or from the majority fusion rule, when thresholded sensing information is utilized.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".