Utilization of unlicensed spectrum in cognitive radio networks: A probability-based approach
Bibliographic record
Abstract
In Cognitive Radio Networks (CRNs), efficient utilization of available channels on the primary spectrum leads to an improvement in the network performance and a reduction of collisions and interference between CRN users. This requires the secondary transmitters to have prior knowledge of the number of primary channels predicted to be available and the periods of time they can be maintained. This allows secondary transmitters to efficiently schedule their data, while satisfying the constrains of the delivered application. In this paper, we use the probability of obtaining an idle set of primary channels to model the probability of holding the available PUs channel(s) for a period of time adequate to deliver the scheduled data for a specific secondary user. The primary users (PUs) channels are assumed to independently follow the two-state Markov Model in changing their availability state. We consider different cases for the PUs activity levels over CRN channels, and compare the approximated availability distribution in each case against the exact one, using extensive numerical simulations for validation. These models are then employed to obtain the desired channel(s) holding probability for two cases of secondary users existence in a CRN.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".