Interference Aggregation in Spectrum-Sensing Cognitive Wireless Networks
Bibliographic record
Abstract
The increasing demand for the radio spectrum along with the inefficient usage of the licensed bands has led the regulatory bodies to consider opening up the under-utilized licensed frequency bands for dynamic access by unlicensed users. Such dynamic spectrum access is envisioned to resolve the spectrum scarcity by allowing unlicensed users to opportunistically utilize the white spaces across the licensed spectrum on a non-interfering basis. Cognitive radio networks offer a promising realization of this novel paradigm, thanks to their ability to autonomously identify the white spaces through spectrum sensing. Implementation of such networks, however, requires a model translating the regulatory constraint on the aggregate interference to the system-and device-level design parameters. In this paper a statistical model of interference aggregation in spectrum-sensing cognitive radio networks is developed. In particular, distribution of the aggregate interference is characterized in terms of parameters such as sensitivity, transmitted power, and density of the cognitive radios as well as the underlying propagation environment. The model is further extended to account for the effect of cooperative spectrum sensing on the distribution of the aggregate interference.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".