Joint Quantization and Confidence-Based Generalized Combining Scheme for Cooperative Spectrum Sensing
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Bibliographic record
Abstract
Cooperative spectrum sensing can effectively protect licensed users from harmful interference and satisfy the quality-of-service requirements of secondary users (SUs) in cognitive radio networks. In this paper, a novel joint quantization and confidence-based generalized (JQCG) combining scheme is proposed. A centralized approach is considered for cooperative spectrum sensing with SUs using the energy detector as a local spectrum sensing scheme. The confidence level is calculated using a fuzzy logic membership function for each cooperative SU. In the proposed JQCG combining scheme, each cooperative SU transmits multiple bits instead of transmitting one bit (hard combining) or the complete test statistic (soft combining) to report local sensing results to the fusion center (FC). We also derive optimal weights (in Neyman–Pearson sense) for each quantized level at the FC to maximize the probability of detection for a given false alarm probability. The proposed JQCG combining scheme is validated by extensive simulation results showing that it has a comparable performance to the soft combining scheme with less overhead. Extensive computer simulations show that the JQCG combining scheme significantly outperforms the hard combining and existing quantized schemes for cooperative spectrum sensing.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 it