Communication-Efficient Decentralized Change Detection for Cognitive Wireless Networks
Why this work is in the frame
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Bibliographic record
Abstract
Spectrum sensing constitutes a key functionality of a cognitive radio (CR), and sensing devices are required to detect a change in spectrum occupancy as quickly as possible. A new decentralized change detection framework is developed for cognitive wireless networks, where local sensors are memoryless, receive independent observations, and no feedback from the fusion center. In addition to traditional criteria of detection delay and false alarm rate, we introduce a new constraint: the number of communications between local sensors and the fusion center. This communication metric is able to reflect both the cost of establishing communication links as well as overall energy consumption over time. The proposed detection scheme minimizes detection delay with constraints on both false alarm rate and number of communications. Simulation results are investigated to explore the tradeoffs in parameter choices of the proposed algorithm.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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