Combined Fusion Rules in Cognitive Radio Networks Using Different Threshold Strategies
Bibliographic record
Abstract
Compromising the performance and overhead is a crucial factor in designing cognitive radio networks (CRNs). One way to achieve this goal is to combine different fusion rules for a CRN with multiple clusters of cognitive radios (CRs). This paper proposes a new adaptive combination algorithm to balance between detection performance of a CRN and its reporting overhead through combining different fusion rules over the CRN. Initially, the paper describes how to combine hard decision, i.e., one-bit, and soften-hard decision, i.e., two-bit, fusion rules over a CRN with multiple clusters of CRs using different strategies. Simple combination and modified combination strategies, to consider a trade off between performance improvement and incurred reporting overhead, are considered. The paper adopts different threshold strategies to implement the proposed combinations. Moreover, the proposed algorithms are examined under the Rayleigh fading channel model and simulated to investigate their detection performance and to compare their detection performance with existing works. The simulation results show that the adaptive threshold strategy outperforms the two proposed fixed threshold strategies and conventional fusion schemes.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".