Stopping Rule-Driven Channel Access in Multi-Channel Cognitive Radio Networks
Bibliographic record
Abstract
In this paper, we propose a stopping rule-driven channel access scheme for a secondary user pair in multichannel cognitive radio networks (CRNs), aiming to achieve a desired tradeoff between channel sensing and channel access. In the proposed approach, we first formulate the sensing-access tradeoff problem as a 1-stage look-ahead stopping problem. We then derive two stopping conditions, namely power-limited stopping and bandwidth-limited stopping, whereby a desired tradeoff between sensing overhead and throughput increase can be achieved. Once a stopping condition is reached, a secondary user pair stops sensing and starts accessing previously sensed free channels for packet transmission. Simulation results show that, in the case of perfect sensing, the proposed approach outperforms a greedy approach by at least 80% in terms of throughput. Imperfect sensing and its impact are also addressed and evaluated.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".