An FPGA implementation of RM-BTC Codec using Log-MAP algorithm
Bibliographic record
Abstract
Due to their powerful error correcting capability and superior coding gain, Turbo Codes are used in 3rd generation wireless and satellite communication systems. For these applications, efficient implementation of Turbo Codes, i.e., development of codec providing high throughput with small chip area and low power consumption is of growing importance. In this thesis, Turbo Code using Reed-Muller code as its constitute code is implemented in VHDL and logic synthesis is executed. The Max-Log-MAP algorithm is used due to its significantly reduced complexity and negligible performance degradation from MAP algorithm. The implementation of codec mainly focuses on achieving the smaller chip area and lower power dissipation, and target to device Virtex-E FPGA. For this purpose, the system and module level optimization of codec architecture is carefully considered through the parallelism and pipeline, interleaving technique, function unit sharing and memory access. The quantization and finite accuracy are also discussed. The simulation in RTL level on a wide variety of test vectors is done, and results show that the encoder/decoder execute properly and correct functionality is realized. The synthesis reports show that chip utilization is reasonable and more resource remains for future improvement.
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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 | 0.000 | 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) | 0.006 | 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".