Decoding techniques for alternate-relaying BICM cooperative systems
Bibliographic record
Abstract
In this paper, we propose the use of bit-interleaved coded modulation in alternate-relaying decode-and-forward cooperative communication systems. At the destination, we exploit the interference signal, which results from the simultaneous transmission of data streams through both direct and one of the relay channels to develop an optimal detector. It is shown that the proposed detector can be implemented by parallel concatenating maximum a posteriori (MAP) algorithms and demappers to the decoders. The detector exchanges soft information between the decoders and the MAP algorithms in an iterative way for performance improvement. The proposed optimal detector requires a long delay as the destination has to receive and store the entire frame before performing data detection. To avoid this, a sub-optimal detector is also proposed. Unlike the optimal detector, the sub-optimal one exploits two consecutive received packets to decode one packet. It turns out that the sub-optimal detector has less reduced delay, complexity, memory size, and bandwidth loss with a slight increase of the bit-error-rate. Extensive simulation results are 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".