Optimal sensing order in cognitive radio networks with channel stability and traffic differentiation
Bibliographic record
Abstract
Cognitive radio networks (CRNs) benefit from several features, such as decision-making, spectrum-awareness and reconfigurability, which enable them to perform spectrum migration in order to recover the link when the operating channel becomes occupied. The time spent for channel migration and recovery is a function of the order in which the channels are selected to be sensed. For optimal sensing order, the parameters which have mostly been considered in the literature are the availability and the quality of the channels. Another important parameter is the channel stability, which is the duration that a channel remains continuously available. We extend in this paper a single-slot model to propose novel decision making dynamic programming (DP) models where availability, quality and stability of the channels are taken into account. Considering the need for differentiation in cognitive radio network, we also propose a differentiated dynamic programming model considering different classes of traffic where the sensing order is determined based on an aggregated cost function. Simulation results show the superiority of decision-making based on DP models compared to other common schemes such as myopic, random or average-based.
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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 | 0.001 |
| 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".