Low-complexity SNR-based channel pairing in multichannel MABC-based two-way relaying
Bibliographic record
Abstract
We consider two-way amplify-and-forward relaying in a multichannel system with two end users and a single relay. A two-slot multi-access broadcast (MABC) relaying strategy is considered. For given power allocation, we investigate into the problem of subchannel pairing at the relay to maximize the achievable sum rate in the network. Unlike in the oneway relaying case, our result shows that there exists no explicit SNR-based subchannel pairing strategy that is optimal for sum-rate maximization for two-way relaying. Nonetheless, we propose a low-complexity SNR-based suboptimal pairing scheme, SNReff-Greedy algorithm, using an effective SNR as the metric for pairing. For N subchannels, the proposed scheme has O(N2log N) complexity, as opposed to O(N3) complexity in obtaining the optimal solution. Simulations demonstrate the effectiveness of the proposed scheme in performance as compared to the optimal pairing and other suboptimal schemes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".