Optimum/sub-optimum detectors for multi-branch dual-hop amplify-and-forward cooperative diversity networks with limited CSI
Bibliographic record
Abstract
We study the optimum maximum-likelihood (ML) detection and sub-optimum detection for a multi-branch dual-hop cooperative diversity network with limited channel state information (CSI). Compared to the full CSI strategy, the signalling overhead at each relay involved with the limited CSI is reduced by 50%. We derive optimum ML detection with the limited CSI, which involves numerical integral evaluations. We also propose two closed-form sub-optimum detection rules of low complexity. It is shown that the first sub-optimum detection has almost identical performance to the optimum ML detection when Gaussianity in the added noise dominates, and the second sub-optimum detection has almost identical performance to the optimum ML detection when non-Gaussianity dominates. Finally, we propose a hybrid sub-optimum detection and demonstrate that its performance is almost identical to that of the optimum ML detection for general cases.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".