Can half-duplex be simply derived from full-duplex communications?
Bibliographic record
Abstract
We consider a discrete memoryless channel between two users and a destination in half-duplex mode implemented by time division. Each transmission block of length n is divided into 3 time slots with variable durations. During the first two time slots, each user alternatively transmits and receives, while during the last time slot, they both transmit to the destination. Even though within each time slot, the channel is similar to a (possibly degraded) broadcast or multiple access channel with known capacity, the capacity of the half-duplex channel cannot be simply derived from these components because of the possibility for joint decoding at the destination over all 3 time slots. We analyze achievable rate regions obtained by superposition encoding, partial decode-forward relaying at each user and two different decoding techniques at the destination. In the first technique, the destination performs separate decoding in each time slot, while in the second one, it performs joint decoding over all 3 time slots. Numerical results for the Gaussian channel show that joint decoding achieves a strictly larger rate region than separate decoding, and both approach the performance of the full-duplex cooperative scheme by Willem et al. as the inter-user channel quality increases.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".