Performance of Cooperative Sensing at the MAC Level: Error Minimization Through Differential Sensing
Bibliographic record
Abstract
Efficient operation of cognitive personal area networks (CPANs) necessitates accurate and efficient sensing of the primary user activity. This is accomplished in a cooperative manner by a number of nodes in the CPAN; the results of sensing are combined by the CPAN coordinator to form a comprehensive and timely channel map. The error of the sensing process is affected by various factors, including the ratio of the number of sensing nodes to the number of channels. In this paper, we present a probabilistic model of the sensing process and derive an analytical solution for the minimum number of sensing nodes that keeps the sensing error below prescribed limits. Then, we discuss three differential sensing policies in which separate sets of sensing nodes target idle and active channels only and show that the policy in which idle channels are given priority, but not exclusive treatment, achieves the best performance, as measured by the number of channels for which the information in the channel map is erroneous and the mean duration of that erroneous information.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".