Selective relaying in multi-relay networks with feedback delays and adaptive modulation
Bibliographic record
Abstract
This paper evaluates the performance of adaptive modulation in multi-relay networks with selective relaying, under Nakagami-m fading. In the system model, the source decides independently whether to forward the source message to the destination via the best (partial relay selection) relay path or direct path by comparing the end-to-end instantaneous signal-to-noise ratio (SNR) at the destination, which is independent of the modulation scheme. Adaptive discrete-rate M-ary quadrature amplitude modulation with fixed switching thresholds is implemented by dividing the SNR region into five modes. In particular, impact of imperfect (outdated) channel estimation due to feedback delay is quantified for relay selection. We derive the cumulative distribution function for the upper-bound of end-to-end SNR in closed-form. Further, lower-bounds of outage probability and average bit error rate, and upper-bound of spectral efficiency are derived in closed-forms. Monte Carlo simulation results validate our numerical analysis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".