A Generalized Switching Policy for Incremental Relaying with Adaptive Modulation
Bibliographic record
Abstract
In this paper, we consider how to improve the capacity of wireless communication networks that employ cooperative diversity techniques. For Amplify and Forward (AF) fixed relaying scheme with adaptive modulation, it has been shown that the scheme can increase transmit diversity but with the expense of a reduction in the spectral efficiency. An efficient Generalized Switching Policy (GSP) for AF incremental relaying with adaptive modulation is proposed to maximize the average spectral efficiency while maintaining the target Bit Error Rate (BER). The performance of the GSP over independent nonidentical Rayleigh fading channels is derived in terms of average spectral efficiency, average BER, and outage probability, then verified by using Monte Carlo simulation. It is shown that the GSP not only outperforms both the conventional direct transmission and the AF fixed relaying but also the Outage Switching Policy (OSP) of the AF incremental relaying proposed recently.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".