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 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 | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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".