Optimum HDAF Relay-Assisted Combining Scheme with Relay Decision Information
Bibliographic record
Abstract
For single-input multiple-output (SIMO) systems, Maximum Ratio Combining (MRC), employed at the receiver, achieves the best performance compared to other combining schemes in the literature, such as Selection Combining (SC) and Equal Gain Combining (EGC). However, for cooperative relay-based systems, MRC has limited performance due to the lack of relay decision information awareness at the destination combiner. To overcome this limitation, this paper proposes a new optimum combining scheme, which is being demonstrated for Hybrid-Decode-Amplify-Forward (HDAF) cooperative system. This scheme utilizes relay decision information in the form of the number of errors per received packet over the source-relay link. The derivation of the optimum combining scheme is based on a mathematical model that utilizes conditional error probability. The improved performance of the proposed optimum combining scheme is demonstrated through analytical results and Monte Carlo simulations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".