Joint Optimal Threshold-Based Relaying and ML Detection in Cooperative Networks
Bibliographic record
Abstract
This paper proposes two detection schemes for cooperative networks comprising a source, a relay and a destination. The relay is assumed to operate in a half-duplex mode and it employs decode-and-forward (DF) relaying. The proposed schemes involve combining threshold-based relaying and maximum likelihood (ML) detection at the destination. We consider both signal-to-noise ratio (SNR)-based and log-likelihood (LLR)-based thresholding. Assuming binary phase shift keying (BPSK), we first derive the ML detector as a function of the threshold used at the relay node. Then, we obtain the optimal thresholds by minimizing the end-to-end bit error rate performance. In deriving the ML performance, we follow an approach that is different from existing approaches and is more straightforward. We compare the performance of the proposed schemes and show that they significantly outperform all existing counterpart detection methods.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".