A Learning-Based Distributed Spectrum Sensing Mechanism for IEEE 802.22 Wireless Regional Area Networks
Why this work is in the frame
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Bibliographic record
Abstract
It is now indisputable that the performance of cognitive radio networks is closely subject to the accuracy and reliability of the inherent spectrum sensing process. In this regard, the development of an efficient sensing mechanism is an imperative task, the performance of which not only relies on the choice of the sensing function, but it substantially depends on the efficiency of the sensing data fusion, i.e. the combining of outputs from individual sensing functions. Due to its importance as well as the lack of efficient algorithms, the spectrum sensing data fusion was left as open issue in the cognitive radio IEEE 802.22 standard for wireless regional area networks (WRANs). In this research, we address this open issue by proposing a novel distributed sensing algorithm for WRANs, named single-channel learning-based distributed sensing (SC-LDS). This algorithm is self-trained, stable, and compensates for fault reports using a reward-penalty approach. Moreover, it exhibits more uniform performance in all traffic regimes, is fair (reduces the false-alarm/mis-detection gap), adjustable to different application needs, and bandwidth efficient. Simulation results unanimously corroborate that the proposed SC-LDS algorithm outperforms other techniques such as the AND, OR and VOTING rules.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 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 it