Optimal channel pairing and power allocation for multi-channel multi-hop relay networks
Bibliographic record
Abstract
Abstract. We study the problem of channel pairing and power allocation in a multi-channel, multi-hop relay network to enhance the end-to-end data rate. OFDM-based relays are used as an illustrative example, and the amplify-andforward and decode-and-forward relaying strategies are considered. Given fixed power allocation to the OFDM subcarriers, we observe that a sorted-SNR subcarrier pairing strategy is data-rate optimal, where each relay pairs its incoming and outgoing subcarriers by their SNR order. For the joint optimization of subcarrier pairing and power allocation, we show that it is optimal to separately consider the two subproblems, for both individual and total power constraints. This separation principle significantly reduces the computational complexity in finding the jointly optimal solution. We further establish the equivalence between sorting SNRs and sorting channel gains in the jointly optimal solution, which allows simple implementation of optimal subcarrier pairing at the relays. Simulation results are presented to demonstrate the performance gain of the jointly optimal solution over some suboptimal alternatives. 1
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".