Mitigating Error Propagation in Two-Way Relay Channels with Network Coding
Bibliographic record
Abstract
In relay networks, error propagation at the relay nodes degrades the performance of the system. To combat that effect, it has been suggested to implement a reliability threshold at the relay to control error propagation. Specifically, the relay calculates log-likelihood ratio (LLR) values for the bits sent from the source. These values are subjected to a threshold to selectively forward bits that are most reliable and discard bits that are less so, resulting in less errors propagating to the destination. We investigate the application of this technique to a network-coded two-way relay channel where the relay is assisting two sources simultaneously. We first consider network-coded systems without channel coding, and then consider network-channel coded systems. We examine two modes of thresholding, one based on the individual bits, and the other based on the combined bits. We provide the full analysis for the bit-error rate (BER) performance of both thresholding modes and optimize the thresholds accordingly. We demonstrate that the optimum thresholds based on both modes give similar performances and are far better than the case of no thresholding. We also consider the performance of the proposed thresholding techniques for network-channel coded systems. We present several numerical examples that illustrate the efficacy of employing thresholding at the relay nodes (for networks with and without channel coding).
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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 | 0.008 |
| 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".