Outage Performance of the Primary Service in Spectrum Sharing Networks
Bibliographic record
Abstract
In this paper, we utilize stochastic geometry to analyze the primary service (PS) outage performance for spectrum sharing in Rayleigh fading environment. Using this approach, the impacts of the secondary service (SS) parameters and wireless environment on the PS outage probability are analyzed. We further obtain a closed form for the PS outage probability. The maximum SS transmitter node density for a given outage probability constraint of the PS is then obtained. We also investigate the impact of secondary spectrum sensing on the PS outage probability. A novel approach is further proposed that provides tight approximation for the PS outage probability. The results of the proposed approach are then validated through analysis and simulations. We then consider power control in the secondary network and show that the truncated channel inversion power control significantly decreases the PS outage probability. Cases with centralized and decentralized cooperative spectrum sensing are also studied, and their corresponding PS outage probabilities are analyzed. Mean spatial throughput of the SS is also analyzed. We further investigate the impact of the PS outage constraint on the spatial throughput of the SS. Extensive simulations confirm our analytical derivations.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| 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".