Channel Sensing-Order Setting in Cognitive Radio Networks: A Two-User Case
Bibliographic record
Abstract
This paper investigates the sensing-order problem in two-user multichannel cognitive medium access control. When adaptive modulation is not adopted, although brute-force search can be used to find the optimal sensing-order setting of the two users, it has huge computational complexity. Accordingly, we propose two suboptimal algorithms, namely, the greedy search algorithm and the incremental algorithm, which have comparable performance with that of brute-force search and have much less computational complexity. It is shown that, with a high probability, either suboptimal algorithm can reach an optimal point if a backoff mechanism is used for contention resolution. When adaptive modulation is adopted, it is observed that the traditional stopping rule does not lead to an optimal point in the two-user case. Furthermore, we demonstrate that the adoption of adaptive modulation affects the optimal sensing-order setting of the two users, compared with the case without adaptive modulation. These findings imply that the stopping rule and the sensing-order setting should be jointly designed from a systematic point of view.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".