Simplified maximum‐likelihood detectors for full‐rate alternate‐relaying cooperative systems
Bibliographic record
Abstract
A key issue in the full‐rate alternate‐relaying cooperative communication systems is the interference which is caused by the simultaneous transmission of the source and one of the relays. In this study, the authors propose maximum‐likelihood (ML) detectors to mitigate the interference in such systems. Unlike previous work in which interference cancellation is required at the destination, the authors exploit the interference signal as a beneficial resource to develop an optimal detector. It is shown that the optimal detector can be implemented by parallel Viterbi algorithms. The major drawback of the proposed optimal detector is the delay because the destination has to receive and store the entire frame before performing data detection. Owing to the inevitable delay restriction, a 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 significantly reduces the required delay, memory size and bandwidth loss, with a slight increase of the bit‐error‐rate and the computational complexity. Extensive simulation results have been presented to demonstrate the effectiveness of the proposed detectors.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".