Partial decode-forward binning for full-duplex causal cognitive interference channels
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Bibliographic record
Abstract
The causal cognitive interference channel (CCIC) is a four-node channel, in which the second sender obtains information from the first sender causally and assists in the transmission of both. We propose a new coding scheme called Han-Kobayashi partial decode-forward binning (HK-PDF-binning), which combines the ideas of Han-Kobayashi coding, partial decode-forward relaying, conditional Gelfand-Pinsker binning and relaxed joint decoding. The second sender decodes a part of the message from the first sender, then uses Gelfand-Pinsker binning to bin against the decoded codeword. When applied to the Gaussian channel, this HK-PDF-binning essentializes to a correlation between the transmit signal and the state, which encompasses the traditional dirty-paper-coding binning as a special case when this correlation factor is zero. The proposed scheme encompasses the Han-Kobayashi rate region and achieves both partial decode-forward relaying rate for the first user and interference-free rate for the second user.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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