SNR-based channel pairing design in multichannel TDBC-based two-way relaying
Bibliographic record
Abstract
We consider two-way amplify-and-forward relaying in a multichannel system with two terminal nodes and one relay nodes, where the time-division broadcast (TDBC)-based two-way relaying strategy is used. Given power alloation, we investigate the design of subchannel pairing at the relay to maximize the sum rate of the system. With broadband systems of large number of subchannels, our focus is on designing efficient subchannel pairing strategies. We first show that, unlike in the one-way case, there exists no explicit SNR-based pairing strategy leading to sum-rate maximization. Instead, we formulate the pairing optimization as an axial 3-D assignment problem which is NP-hard, and propose an iterative optimization method to solve it with complexity O(N3). Based on SNR over each subchannel, we also propose sorting-based algorithms for scenarios with and without direct link, with a low complexity of O(N logN). The simulation results also show the proposed algorithm offers the performance very close to the iterative optimization method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".