VHDL Implementation of a Turbo Decoder With Log-MAP-Based Iterative Decoding
Bibliographic record
Abstract
Turbo code is one of the most significant achievements in coding theory during the last decade. By concatenating two simple convolutional codes in parallel, it has been shown that transmission systems employing turbo codes could offer near-capacity performance. More importantly, by employing a suboptimal iterative decoding structure with soft-in/soft-out (SISO) maximum a posteriori-probability (APP) decoding algorithm, the near-capacity performance is achievable at a feasible decoding complexity. Given the outstanding performance of turbo code, the challenge now is to implement it into various communication systems at affordable decoding complexity using current very large scale integration (VLSI) technologies. In this paper, we first investigated the existing four different turbo decoding algorithms. Comparisons of both their performances and implementation complexities were performed. Log-maximum a posteriori (MAP) -based turbo decoding was found to offer the best performance-complexity compromise. A register-transfer-level (RTL) 12-bit fixed-point turbo decoder based on Log-MAP algorithm was then designed and simulated using VHDL as the hardware description language. The implemented RTL model was verified by comparing its performances with those obtained from a C-language implementation of the same turbo decoder.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".