Cooperative versus Non-Cooperative Han-Kobayashi Signaling: Outage Performance
Bibliographic record
Abstract
We propose cooperative Han-Kobayashi (H-K) signaling schemes and investigate their performance in comparison with non-cooperative H-K signaling. In the cooperation scenario, the near user relays the base station (BS) common message or the far user’s message to increase the received signal-to-interference-plus-noise ratio of the far user. We evaluate the outage performance of the non-cooperative and cooperative signaling schemes. Specifically, we obtain the exact outage probability (OP) of the non-cooperative strategy, and closedform approximations for the OP of the cooperative schemes. As demonstrated in the comparative results, by setting an appropriate BS’s power budget and power allocation, the non-cooperative mechanism has the potential to excel in OP. Finding the best power allocation to achieve the least outage with non-cooperative H-K is not trivial, whereas allocating a small level of the BS’s transmit power to the far user’s message, and then relaying the said message with a suitable power can yield significant improvements in the outage performance and make the cooperative approach superior.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".