On bounds for the cognitive multiple access Z-Interference Channel
Bibliographic record
Abstract
The Cognitive Multiple Access Z-Interference Channel consists of a MAC channel that receives interference from an external transmitter. In a previous work we studied this model where both MAC users are cognitive and showed that an encoding scheme based on Dirty Paper coding attains the capacity region for the Gaussian case. In this work we assume that only one MAC user is cognitive and show that the non-cognitive MAC user can still benefit from the cognition of the other MAC user compared to the case where none of the MAC users are cognitive. We compare the achievable rate region obtained for our model with those for the cases where interference is treated as noise and where part of the primary user's message is decodable at the MAC receiver.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".