On the design and performance of TDBC-based bi-directional network beamforming
Bibliographic record
Abstract
We study and compare the performance of two bi-directional relay network beamforming schemes, namely time division broadcast channel (TDBC) strategy and multiple access broadcast channel (MABC) strategy, under joint optimal power control and beamforming design. Under the presence of a direct link between the two transceivers, we first design the optimal TDBC-based bi-directional network beamformer to minimize the total power consumption in the network subject to quality of service (QoS) constraints. We obtain the optimal second-order cone programming based solution as well as fast gradient-based solution to this optimization problem. With this result, we then numerically compare the beamforming performance of the TDBC approach to that of the MABC approach developed in [1]. We show that the TDBC approach can outperform the MABC approach, under the same rate constraint, provided that the direct link is strong enough.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| 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".