A model for bursty PU channel and its impact on the study of cognitive radio networks
Bibliographic record
Abstract
In this paper, we investigate the impact of channels that have a bursty nature in a cognitive radio network scenario. Our goal is to design a general statistical model that can handle bursty primary user (PU) channel usage. The proposed model describes idle periods with a discrete platoon arrival process (PAP) and describes busy periods with a discrete phase type (PH) distribution. This channel model is referred to as a PAP-PH process. We further introduce a proactive access scheme as the potential application of the proposed channel model and use it to compare the performance of the proposed model, in terms of spectrum utilization and interference probability, with two traditionally encountered channel usage models, i.e., the geometrically distributed idle-busy period model and the phase type distributed idle-busy period model, under both bursty and non-bursty channel scenarios. Numerical results show that with the proposed model, the proactive access scheme can guarantee the interference threshold to the PU and can be used for both bursty and non-bursty spectrum use patterns.
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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".