Multilevel Coding with General Decoding Metrics and Rateless Transmission
Bibliographic record
Abstract
Multilevel coding (MLC) is the main contender to the celebrated bit-interleaved coded modulation (BICM) technique for combining binary error-control codes with multilevel constellations. Although MLC has a more complex encoding-decoding structure, it can achieve a larger rate in a number of important scenarios such as multiple-input multiple-output (MIMO) and orthogonal modulation transmission. In this paper, we consider two issues related to the application of MLC. First, we examine the use of general decoding metrics in MLC, including mismatched metrics that arise from approximations to reduce detection complexity. We make use of recent advances in the analysis of BICM and apply those techniques to individual MLC transmission layers. Our contributions include rate analysis and metric-mismatch correction to improve throughput performance of MLC. Second, we consider the combination of MLC with binary rateless codes. Such a combination eliminates the need to carefully design code rate for each MLC layer. In slow fading environments, rateless coding can also seamlessly adapt to the instantaneous channel quality and achieve an increased average throughput compared to a fixed-rate MLC transmission. However, due to the MLC structure, we show that a naive combination of MLC and rateless coding can cause a significant rate loss. Thus, we propose a novel rotation rateless scheme which preserves the rate advantage of MLC over BICM. We provide relevant examples with MIMO, frequency-shift keying (FSK), and pulse-position modulation (PPM) signaling to demonstrate that our scheme can achieve throughput gains compared to BICM in a variety of transmission 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 | 0.007 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".