Impact of User Collaboration on the Performance of Sensing-Based Opportunistic Spectrum Access
Why this work is in the frame
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Bibliographic record
Abstract
Spectrum scarcity is becoming a major issue for service providers interested in either deploying new services or enhancing the capacity for existing applications. On the other hand, recent measurements suggest that many portions of the licensed (primary) spectrum remain unused for significant periods of time. This has led the regulatory bodies to consider opening up under-utilized licensed frequency bands for opportunistic access by unlicensed (secondary) users. Among different options, sensing-based access incurs a very low infrastructure cost and is backward-compatible with the legacy primary systems. In this paper we investigate the effect of user collaboration on sensing- based secondary access in terms of critical parameters such as the spectrum utilization, the required signal-to-noise-ratio and the detection time. As suggested by our analysis and simulation results, collaboration may improve the opportunistic spectrum access significantly.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it