Diversity Analysis of Relay Assignment in Cooperative Networks Based on Sum-Rate Criterion
Bibliographic record
Abstract
In this paper, we consider relay assignment in cooperative networks based on the sum-rate criterion. We show that this scheme achieves full diversity, assuming that all the end-to-end (E2E) channels are independent. Our analysis is motivated by the fact that there are many algorithms in the literature to find the permutation that maximizes sum rate; however, the diversity order achieved by each user through the use of this criterion is not statistically analyzed. We perform this analysis in an amplify-and-forward (AF) relay network comprising$N$source–destination pairs and$N$relays where each relay can serve only one-source destination pair. However, the diversity analysis holds true for other network configurations where the E2E channels are independent Rayleigh fading channels. To perform this analysis, we first propose a new general method to calculate the diversity order of fading channels at a high SNR. In the previous method, the diversity order was expressed in terms of the Taylor expansion of the random SNR's probability density function (pdf) at the origin, but that method fails to calculate the diversity when the pdf is not well behaved at the origin. (The pdf is finite, but its derivative is not defined.) Our proposed method is a unifying method that works wherever the previous method works and also where the pdf of SNR is not well behaved.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".