Distributed coded Cooperation for Relay Channels Operating in the Decode-and-Forward Mode
Bibliographic record
Abstract
We introduce coded cooperation diversity using relay nodes in the decode-and-forward (DF) mode, in which the codewords of the source node are partitioned and transmitted through independent fading channels to both relay and destination nodes, to achieve remarkable gains over a noncooperative system. The main difference between the distributed and the noncooperative space-time coding is that the link between the source and the relay node is not error-free in the distributed case, as opposed to the noncooperative case. In this work, we take into account the errors in the source-relay link and derive upper bounded expression for the symbol error rate on DF relaying in the case of M-ary phase shift keying (M-PSK) transmission. Our analytical results show that the maximum diversity order is achieved provided that the source-relay link is more reliable than the other links. Otherwise, the diversity degrades. This is unlike the case when the relay nodes operating in the DF mode are based on symbol-by-symbol decoding and forwarding. We present several numerical examples to support our analytical expression.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".