Distributed Linear Constellation Precoding with BICM/BICM-ID in Two-Way Relaying Communications
Bibliographic record
Abstract
This paper investigates distributed linear constellation precoding (DLCP) for two-way relaying communication systems in conjunction with the techniques of bit-interleaved coded modulation (BICM) and BICM with iterative decoding (BICM-ID). First, a decoding strategy for the relay node that is based on quaternary code representation is developed. Then, the union bounds (for the case of BICM) and error-free feedback bounds (for the case of BICM-ID) on the quaternary digit error probability and bit error probability under network coding in the multiple-access (MA) phase are obtained. Based on the obtained bounds, the impact of DLCP on the error performance is analyzed by considering three error types in the MA phase. It is shown that type-3 errors need to be carefully taken into account in the design of a DLCP scheme. By developing a performance metric related to type-3 errors, the design parameter of DLCP is optimized when BICM is used, whereas it is shown that DLCP is not needed when BICM-ID is used. Extensive simulation results are provided to corroborate the analysis and demonstrate the performance superiority of the proposed decoding strategy over the one that directly decodes the exclusive-OR (xor) code. For the case of BICM, the performance advantage achieved by properly designing DLCP is also illustrated.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".