Performance of cooperative spectrum sensing with correlated cognitive users' decisions
Bibliographic record
Abstract
Cooperative spectrum sensing is employed in cognitive radio network to reliably detect the primary users' transmissions by fusing the sensing data of individual secondary users. In this paper, we study the performance of cooperative spectrum sensing, in terms of the system probability of detection, when the secondary users' local decisions are correlated. We use a correlation model that is indexed by a single parameter and fix the fusion rule to one of three decision rules which are the OR, AND and Majority Voting rules. Our results show that the performance of cooperative spectrum sensing degrades with the increase in correlation between the secondary observations for all the fusion rules considered. We also show that, whether the OR or Majority Voting rule is superior depends mainly on the correlation index. When the secondary users' local decisions are independent, the Majority Voting rule outperforms the OR and AND fusion rules. However, as the correlation between the local decisions increases, the OR fusion rule outperforms the other two rules. Also, as the correlation index increases, for the same system probability of false alarm, higher signal-to-noise ratio is required to be received at the secondary users to achieve the same system probability of detection for all the fusion rules considered.
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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 | 0.007 | 0.031 |
| 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 | 0.002 |
| 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".