An Improved Max-Log-MAP Algorithm for Turbo Decoding and Turbo Equalization
Bibliographic record
Abstract
This paper proposes an improved Max-Log-maximum a posteriori (MAP) algorithm for turbo decoding and turbo equalization. The proposed algorithm utilizes the MacLaurin Series to expand the logarithmic term in the Jacobian logarithmic function of the Log-MAP algorithm. In terms of complexity, the proposed algorithm can easily be implemented by means of adders and comparators as this is the case for the Max-Log-MAP algorithm. In addition, simulation results show that the proposed algorithm performs very closely to the Log-MAP algorithm for both turbo decoding over additive-white-Gaussian-noise channels and turbo equalization over frequency-selective channels. Further, it is shown than even in a high-loss intersymbol-interference channel, the proposed algorithm preserves its performance close to that of the Log-Map algorithm, while there is a wide gap between the performance of the Log-MAP and Max-Log-MAP turbo equalizers
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".