Optimum beamforming in the broadcasting phase of bidirectional cooperative communication with multiple decode-and-forward relays
Bibliographic record
Abstract
This letter focuses on the broadcasting phase of bidirectional cooperative networks with multiple decode-andforward relays. In this phase, the relays first combine the information-bearing symbols transmitted by the sources, and then broadcast them back to the sources in order to achieve bidirectional communications. Two different combining methods at the relays are considered. The first one is that the relays transmit linear combinations of the information-bearing symbols to the sources by beamforming. We develop an algorithm that can compute the optimum beamforming vector in closed form and this beamforming vector minimizes the outage probability of the bidirectional cooperative network. The second method is that the relays combine the information-bearing symbols by exclusive-or and then transmit them to the sources by beamforming. For this case, we show that the instantaneous signal-to-noise ratios at the sources depend on the values of the information-bearing symbols. Based on [1], the optimum beamforming vector is computed and it minimizes an upper bound of the outage probability of the bidirectional cooperative network.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".