Near ML Performance for Linear Block Codes Using an Iterative Vector SISO Decoder
Bibliographic record
Abstract
In this paper, we present an iterative soft-decision decoding algorithm applicable to binary linear block codes. The algorithm uses a modified Vector SISO algorithm that utilizes soft input information to form candidate codewords. A threshold test is used to determine whether the codeword is likely to be the ML codeword. If the codeword is not accepted, the decoder biases the input vector ?away? from the candidate codeword and the decoding repeats this process up to a maximum number of decodings. Occasionally, this process does not find an acceptable codeword. When this happens the algorithm perturbs the input vector by modification of the sign of the input values and repeats the decoding process. The average complexity of the algorithm at reasonable Eb=N0 is low and the performance is near ML. A detailed description of the algorithm and simulations results are presented for low rate linear block codes. We present results for (80,40,16), (104,52,20) and (168,84,24) quadratic residue codes and (256,191,18) and (128,29,44) extended BCH codes.
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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 | 0.006 |
| 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) | 0.002 | 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".