Detection Techniques for Two-Relays Decode and Forward Cooperative Systems
Bibliographic record
Abstract
In this paper, we propose maximum likelihood (ML) detectors to mitigate the influence of the interference signal for the two-relays full- rate cooperative systems. At the relays, the proposed ML detector is employed by averaging out the interference signal. Furthermore, at the destination, we exploit the interference signal to develop the ML detector. It is shown that the optimal detector is implemented by parallel Viterbi algorithms. The major drawback of the proposed optimal detector is the delay, i.e a destination has to receive and store the whole received packets before performing data detection. Due to the inevitable delay restriction, sub-optimal detector is developed. In contrast with the optimal detector, the sub-optimal detector exploits two consecutive received packets to decode one packet. It turns out that the sub-optimal detector outperforms the optimal detector in terms of the required delay, memory size, bandwidth loss, and computational complexity. Extensive simulation results have been presented to demonstrate the effectiveness of the proposed detectors. Results indicate that the proposed detectors outperform conventional relaying detectors in terms of their bit error rate and packet error rate.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".