New designs for bit-interleaved coded modulation with hard-decision feedback iterative decoding
Bibliographic record
Abstract
Bit-interleaved coded modulation (BICM) is often the method of choice for multilevel transmission over fading channels. If relatively simple forward error-correction (FEC) codes such as convolutional codes are employed, iterative decoding between demapper and FEC decoder can provide significant performance improvements over non-iterative decoding. To keep the complexity of iterative decoding low, the use of hard-decision feedback from FEC decoder to demapper is appealing. However, the price to be paid is a performance degradation due to feedback errors. In this letter, two new demapper designs are developed which are able to strongly mitigate the effect of erroneous feedback. The key ideas are (a) the use of an estimate of the average error rate for the hard-decision feedback and (b) the interpretation of feedback errors as additive impulsive noise. Simulation results show that the proposed designs achieve error rates close to those for iterative decoding with soft feedback, while they maintain the complexity advantage of using hard decision feedback.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".