Design of Channel Coded Heterogeneous Modulation Physical Layer Network Coding
Bibliographic record
Abstract
In a two-way relay channel network (TWRC), the integration of channel coding into symmetric physical layer network coding (PNC) has been well studied, where both sources use exactly the same channel coding and modulation schemes and the relay decodes and reencodes the codewords obtained from the superimposed signals. How to integrate the channel coding into heterogeneous modulation PNC (HePNC), where the sources apply different modulations, is an open issue. In this paper, we propose a channel coded HePNC (CoHePNC) scheme under asymmetric TWRC. For repeat-accumulate (RA) codes applied at the sources, a full-state sum-product decoding algorithm is proposed which enables the relay to decode the superimposed signals from the sources to the raw decoding results firstly, and then re-encode and obtain the network-coded codewords by mapping the raw decoding results according to the proposed bit-level mapping functions. We further optimized the bit-level mapping functions according to the two source-relay channel conditions. Extensive simulation results demonstrated that the proposed CoHePNC outperforms the existing channel coded PNC schemes in terms of the relay decoding error rate and the end-to-end bit error rate under asymmetric TWRC scenarios.
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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 | 0.002 |
| 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) | 0.001 | 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".