Coded Collaborative Spectrum Sensing With Joint Channel Decoding and Decision Fusion
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Bibliographic record
Abstract
This paper considers the integration of channel decoding with fusion based decision, for coded collaborative spectrum sensing (CSS) employing local Neyman-Pearson (NP) testing at each sensor. We derive a belief-propagation (BP) algorithm for joint channel decoding and decision fusion (JCDDF), based on a factor graph model for coded CSS schemes. Using the Lloyd-Max method, we also propose a new methodology for the local sensor to quantize its observation. The design of the quantizer embeds the binary NP test outcome in the quantization bits. Using the JCDDF algorithm, we show that coded CSS paired even with a short (8,4) extended Hamming code outperforms not only uncoded CSS, but also schemes where channel decoding and decision fusion are executed separately. Then, we consider the design of good channel codes for such CSS schemes. We demonstrate that the JCDDF algorithm employing unequal error protection (UEP) coding improves performance and outperforms equal error protection coding. Furthermore, we present a simple code search algorithm for identifying short UEP codes. Using such UEP codes, we finally show that a performance improvement over uncoded CSS can be attained also without bandwidth expansion using higher order modulations.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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 it