Performance analysis of cognitive radio networks with channel assembling and imperfect sensing
Bibliographic record
Abstract
This paper investigates the performance of a wideband cognitive radio network where each cognitive user can assemble multiple primary channels. Two channel assembling schemes are considered: a constant channel assembling (CCA) and a variable channel assembling (VCA). In the variable channel assembling scheme, cognitive users assemble their channels on the basis of the number of detected residual channels that are unoccupied by primary users or cognitive users. The effects of imperfect spectrum sensing (with false alarms and misdetections) are taken into account and it is assumed that spectrum handover is implemented in the secondary network. These channel assembling schemes are analyzed by using Continuous-time Markov chains (CTMC), and the system performance is evaluated in terms of throughput, blocking probability, and forced termination probability. Numerical results show that channel assembling achieves lower forced termination probability, but does not increase achieved system throughput and leads to higher blocking probability. They also show that VCA outperforms CCA in terms of throughput and forced termination probability.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".