Joint Subchannel Pairing and Power Allocation in Multichannel MABC-Based Two-Way Relaying
Bibliographic record
Abstract
We consider amplify-and-forward two-way relaying in a multichannel system with two end nodes and a single relay, with a two-slot multiaccess broadcast (MABC) relaying strategy. We investigate the problem of joint subchannel pairing and power allocation to maximize the achievable sum-rate in the network under the individual power constraints. We propose an iterative approach to solve this challenging mixed-integer programming problem by decomposing it into subchannel pairing optimization and joint power allocation optimization, and solving them iteratively. For subchannel pairing at the relay, we show that, unlike in the one-way relaying case, there exists no explicit SNR-based low-complexity subchannel pairing strategy that is optimal for two-way relaying, and the optimal pairing needs to be performed numerically. Nonetheless, we propose an effective low-complexity suboptimal pairing scheme based on an effective SNR metric. For joint power allocation at all nodes, the optimization problem is nonconvex. We propose an iterative procedure to optimize the power at the two end nodes and at the relay iteratively. Using a problem transformation, we show that each power optimization subproblem turns out to be convex and can be solved efficiently. Our proposed iterative procedure is guaranteed to converge to a locally optimal solution. We then generalize our approach to the weighted sum-rate maximization problem. Simulation results demonstrate the effectiveness of the proposed pairing scheme, as well as the gain of joint optimization approach over other pairing-only or power-allocation-only optimization approaches.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".