Decode-Compress-and-Forward with Selective-Cooperation for Relay Networks
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Bibliographic record
Abstract
We propose a new signal-processing scheme, referred to as Decode-Compress-and-Forward with Selective-Cooperation (DCF-SC). In DCF-SC, the relay dedicates a certain amount of time to listen to the message broadcasted by the source and then performs Soft-Input Soft-Output (SISO) decoding. The relay then quantizes the Log-Likelihood Ratio (LLR) values received from the SISO decoder, encodes them and then transmits to the destination. The Selective-Cooperation condition determines whether the destination will accept or reject relay's collaboration. We consider half-duplex relaying with orthogonal channels at the destination and apply turbo coding at both source and relay nodes. We define a trade-off parameter that determines how much of the relay's time should be dedicated to listening and to transmission. We show by simulations that this trade-off factor has an optimal value for which the Block-Error Rate (BLER) is minimized. We compare the error rate performance of the proposed DCF-SC scheme with that of the Decode-Amplify-Forward (DAF) scheme presented in the literature.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it