Power-optimized amplify-and-forward multi-hop relaying systems
Bibliographic record
Abstract
Optimal power allocation schemes that maximize the instantaneous received signal-to-noise ratio in an amplify-and- forward multi-hop transmission system under short-term (ST) and long-term (LT) power constraints are presented. The optimal power allocation strategy under a ST power constraint requires a centralized architecture for implementation. However, the power allocation solutions to the LT power constraints can be implemented in a decentralized manner. Theoretical expressions for evaluation of the outage probability of the proposed power-optimized multi-hop relaying systems over Rayleigh fading channels are obtained. Numerical results show the superior performance of amplify-and-forward multi-hop relaying systems with the power allocation scheme over those with uniform power allocation. It is shown that at sufficiently large values of signal-to-noise ratio (SNR), an amplify-and-forward K-hop relaying system employing the optimal power allocation scheme under ST power constraint achieves K times better outage performance than that of the corresponding system employing uniform power allocation. It is also shown that the optimal power allocation schemes under LT power constraints provide substantial performance gain at both small and large values of SNR and achieve diversity gain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".