Trellis Termination in Turbo Codes with Full Feedback RSC Encoders
Bibliographic record
Abstract
Trellis termination of turbo codes is critical for maintaining their good performance, especially for short information blocks when the deterministic interleaver is sought to reduce the complexity of signaling the interleaver permutation. To address this problem, this paper introduces a new type of turbo code called the Return to Zero (RZ) turbo code whose both component encoders are brought to the initial (zero) state. Specifically, a general mathematical model is built in this paper to serve as the theoretical foundation of RZ turbo codes. The model is used to compute the state of a general recursive systematic convolutional (RSC) encoder. Based on this model, a unique duo property of certain class (full feedback) of RSC encoders is demonstrated which is further used to introduce a new type of interleaver called the RZ interleaver. This interleaver is capable of processing the input sequence of bits in such a way that it can bring both encoders into the initial state. Simulation results presented in this paper show that the RZ turbo code can achieve almost the same performance as that of the traditional turbo code when the interleaver size is small or medium.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".