Cooperative Sensing With Heterogeneous Spectrum Availability in Cognitive Radio
Why this work is in the frame
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Bibliographic record
Abstract
Cooperative spectrum sensing significantly improves sensing reliability in cognitive radio networks. However, in large-scale secondary networks, spectrum availability is heterogeneous, i.e., secondary users at different locations may observe different primary users, thus having different spectrum availability statuses. Despite the heterogeneity, sensing cooperation is beneficial because spatially proximate secondary users are likely to share the same spectrum availability status. The challenge is in modeling and exploiting spatial correlations to fuse secondary users’ observations and improve sensing performance. This paper develops a cooperation framework to address this challenge, where we model spatial correlations among secondary users via a Markov random field. Finding the maximum posterior probability over the Markov random field achieves sensing cooperation. We thus propose three cooperative sensing algorithms for centralized, clustered, and distributed secondary networks. These algorithms provide superior computation efficiency and less communication overhead compared to existing methods.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it