Diversity Analysis of Multi-User Multi-Relay Networks
Bibliographic record
Abstract
In this paper, we analyze the diversity order of opportunistic scheduling networks with arbitrary numbers of relays and users. We show that the opportunistic selection of the relay-user pair with the best end-to-end signal-to-noise ratio (SNR) among M relays and N users achieves a diversity order in the range of [M + N, MN + N] for amplify-and-forward (AF) relays and in the range of [N, MN + N] for decode-and-forward (DF) relays. Our analysis reveals that the achievable diversity order with AF relays depends on the relative strength of the source-relay (SR) and relay-destination (RD) links, while the achievable diversity order with DF relays depends on the SR link SNR. Based on our analysis, which is verified by simulation results, we show that, for AF relays, the maximum diversity order of MN + N is achieved if the SR link quality is better than the RD link quality, and, for DF relays, if the SR link is sufficiently strong such that the relays always succeed in decoding.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".