Performance Analysis and Optimization of Multiselective Scheme for Cooperative Sensing in Fading Channels
Bibliographic record
Abstract
We propose a multiselective sensing scheme where the primary-user (PU) activity is detected in cognitive radio through cooperation among the different sensing nodes and the fusion center. The proposed cooperative sensing scheme is based on order statistics of the reporting links between the cooperative nodes and fusion center where the links with high signal-to-noise ratios (SNRs) are selected as reliable reporting links. The performance of the proposed scheme is compared with other existing schemes in terms of the probability of detection and probability of false alarm over independent and identically distributed (i.i.d.) and independent nonidentical distributed (i.n.d.) Rayleigh fading channels. Both simulations and analytical results show that the proposed scheme outperforms conventional sensing schemes under different system parameters. Furthermore, we examine the optimum N-out-of-K rule of our scheme under different detection threshold and SNR. Our results show that the proposed multiselective scheme offers improvement in terms of the probability of detection when compared with other existing schemes, such as selection combining (SC), square-law selection (SLS), and general N-out-of-K rule.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".