Comparison between continuous-time asynchronous and discrete-time synchronous iterative decoding
Bibliographic record
Abstract
Conventional iterative decoding with flooding or parallel schedule can be formulated as a fixed-point problem solved iteratively by the successive substitution (SS) method. In this work, we investigate the dynamics of continuous-time asynchronous analog implementation of iterative decoding, and show that it can be approximated as the application of the well-known successive overrelaxation (SOR) method for solving the fixed-point problem. We observe that SOR with the optimal relaxation factor can considerably improve the performance of iterative decoding for short low-density parity-check (LDPC) codes compared to SS. Our simulation results for the application of SOR to belief propagation (sum-product) and min-sum algorithms demonstrate improvements of up to about 0.7 dB over the standard SS for randomly constructed LDPC codes. The improvement in performance increases with the maximum number of iterations and by accordingly reducing the relaxation factor. The asymptotic result, corresponding to infinite maximum number of iterations and infinitesimal relaxation factor represents the performance of analog continuous-time asynchronous iterative decoding. This means that under ideal circumstances continuous-time asynchronous analog decoders can outperform their discrete-time synchronous digital counterparts by a large margin. The proposed model for analog decoding, and the associated performance curves, can be used as an "ideal analog decoder" benchmark for performance evaluation of analog decoding circuits.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".