On the application of BP decoding to convolutional and turbo codes
Bibliographic record
Abstract
We investigate a new approach to decode convolutional and turbo codes by means of the belief propagation (BP) decoder used for low-density parity-check (LDPC) codes. In addition, we introduce a general representation scheme for convolutional codes through parity check matrices. Also, the parity check matrices of turbo codes are derived by treating turbo codes as parallel concatenated convolutional codes. Indeed, the BP algorithm provides a very attractive general methodology for devising low complexity iterative decoding algorithms for all convolutional code classes as well as turbo codes. However, preliminary results show that BP decoding of turbo codes performs slightly worse than conventional maximum a posteriori (MAP) and soft output Viterbi algorithm (SOVA) algorithms which already are in use in turbo code decoders. Since these traditional turbo decoders have a higher complexity, the observed loss in performance with BP is more than compensated by a drastically lower implementation complexity. Moreover, given the encoding simplicity of turbo codes with respect to generic LDPC codes, the low decoding complexity brings about end-to-end efficiency.
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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) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".