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(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ). 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".