On the Spectral Efficiency of Selective Decode-and-Forward Relaying
Bibliographic record
Abstract
Multirelay cooperative relaying enables spatial diversity, often at the expense of spectral efficiency. To alleviate the loss in spectral efficiency due to half-duplex relaying and transmission over orthogonal channels, we propose a novel transmission scheme for selective decode-and-forward (DF) networks. In this scheme, we assume that destination may receive signals from transmitting nodes with different modulation levels. Particularly, we obtain a closed-form expression for both end-to-end (E2E) average error probability and spectral efficiency in such a scheme. Subsequently, using these closed-form expressions and average channel statistics, we perform joint optimization of power allocation and modulation level selection to maximize the E2E spectral efficiency while maintaining a target E2E average error probability and a set of transmit power constraints. Simulation results demonstrate that the transmission scheme proposed herein improves the E2E spectral efficiency significantly, in comparison with the conventional adaptive DF transmission scheme.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".