Variable‐rate adaptive modulation with optimum switching thresholds for cooperative systems with relay selection
Bibliographic record
Abstract
ABSTRACT In this paper, the performance of variable‐rate adaptive modulation schemes in the amplify‐and‐forward cooperative systems with relay selection is analyzed over Rayleigh fading channels. We consider constant power and discrete‐rate adaptive multi‐level modulation techniques. The switching levels required for discrete‐rate adaptive modulation have been determined for two schemes, namely fixed switching levels and optimum switching levels, both respecting a target bit error rate requirements, where in the later scheme, the switching levels are optimally determined in a way that the average spectral efficiency of the system is maximized. Two M‐ary modulation schemes, namely quadrature amplitude modulation and phase shift keying, are considered. Closed‐form expressions are derived for three performance metrics, namely average spectral efficiency, outage probability, and average bit error rate, for two cases: independent and identically distributed fading relay links and independent and non‐identically distributed links. It is shown that, compared with using fixed switching levels, employing optimum switching levels provides a slight improvement in the spectral efficiency and moderate improvements in the signal‐to‐noise ratio gain and in the outage probability of the system. It is also shown that compared with the independent and identically distributed links, independent and non‐identically distributed relay links yield a slight increase in the signal‐to‐noise ratio gain and a slight decrease in the diversity order of the system. Copyright © 2011 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".