On Optimal Power Allocation for Source-Orthogonal Relay-Nonorthogonal Amplify-and-Forward Relaying
Bibliographic record
Abstract
An amplify-and-forward relaying network where the source is orthogonal to all the relays but the relays are nonorthogonal (SORNAF) is studied. The problem of relay power optimization in a SORNAF relaying scheme is formulated as a nonconcave fractional program. Selection amplify-and-forward (SAF) relaying is a possible power allocation strategy previously proposed for the SORNAF scheme. A necessary condition for the optimality of the SAF power allocation is derived in terms of instantaneous channel coefficients, based on which, an upper bound on the SAF optimality probability is calculated. The upper bound approaches zero almost exponentially as the number of relays increases, showing that SAF relaying is asymptotically strictly suboptimal. A suboptimal power allocation, which is significantly superior to the SAF algorithm in terms of outage performance, is also proposed for SORNAF relaying.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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".