Analysis of a Distributed Coded Cooperation Scheme for Multi-Relay Channels
Bibliographic record
Abstract
In this paper, we consider a coded cooperation diversity scheme suitable for relay channels. The proposed scheme is based on convolutional coding, where each codeword of the source node is partitioned into two parts. The first part is transmitted from the source to the relays and destination. The second part is transmitted simultaneously from the source and relay nodes to the destination. All the relay nodes are assumed to be operating in the decode-and-forward (DF) mode. At the destination, the two replicas of the second sub-codeword are combined using maximum ratio combining (MRC). The entire codeword is decoded via the Viterbi algorithm. We analyze the proposed scheme for L-relay channels in terms of its probability of symbol error and outage probability. In that, explicit upper bounds are obtained assuming M-ray 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. However, in both cases, it is shown that substantial performance improvements are possible to achieve over noncooperative coded systems. Several numerical and simulation results are presented to demonstrate the efficiency of the proposed scheme.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".