Identifying boundaries of dominant regions dictating spectrum sharing opportunities for large secondary networks
Bibliographic record
Abstract
An important parameter in determining a spectrum sharing opportunity is the level of interference power that secondary users may generate towards primary users. It is indicated in literature that the aggregate interference power of an infinite network (such as a very large secondary network) is bounded under certain conditions. However, to the best of our knowledge, no work has been devoted to determining the boundary of the dominantly interfering region. In this paper, we identify the smallest portion (dominant region) of the secondary network that would impact spectrum sharing opportunities. Our results show that the dominant region is not necessarily a small region encompassing a few interferers within the proximity of the primary user. Far interferers may tangibly contribute to spectrum sharing decisions when a higher approximation accuracy is required or when a wide exclusion region (within which no secondary users are allowed to transmit) is considered. On the other hand, the dominant region shrinks with the increase in the path-loss exponent or in the level of the interference threshold specified by the primary user or a regulator. Some implications of these results are highlighted. Moreover, the results are anticipated to inspire new ideas for designing MAC protocols for secondary networks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".