Relay selection with time-division broadcast in bidirectional cooperative networks
Bibliographic record
Abstract
We study relay selection (RS) for time-division broadcast (TDBC) in a bidirectional network consisting of two different end-sources and multiple relays. By modifying the well-known proactive RS scheme valid only for unidirectional cooperative networks, we propose a proactive RS with TDBC (P-TDBC) for bidirectional cooperative networks. In the P-TDBC scheme, a single best relay is selected proactively before two consecutive source-transmissions by maximizing the bottleneck of the end-to-end maximal achievable rates in both directions. We then derive the exact outage probability of P-TDBC in closed-form for Es≤ Er, and in a single-integral form for Es>; Er, where Esand Erdenote the transmission powers at each end-source and the selected relay, respectively. Moreover, we derive very tight closed-form upper and lower bounds on the outage probability for Es>; Er. Finally, we study the diversity-multiplexing tradeoff (DMT) and show that the P-TDBC scheme achieves full diversity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".