TS-LDPC analog decoding based on the Min-Sum algorithm
Bibliographic record
Abstract
It has been shown that Min-Sum (MS) algorithms have low complexity in the implementation of analog VLSI decoders compared to the Sum-Product (SP) algorithm. Moreover, Turbo-structured LDPC (TS-LDPC) codes are known to have lower error floor than random LDPC codes. In this paper, Min-Sum and Min-Sum with correction factor algorithms are reviewed and adapted with TS-LDPC codes for future analog VLSI implementation. Simulation results show that the error performance of the Min-Sum algorithm is comparable with the Sum-Product algorithm for the same block length. This means that the lower error floor property of TS-LDPC codes is preserved when MS algorithms are used. Moreover, analog decoder implementation of TS-LDPC codes is studied. To test the suitability of the MS algorithm based TS-LDPC decoder some analog impairments such as mismatch, leakage and noise are considered in the decoding procedure of TS-LDPC codes. In each case, it is shown that the degradation of the error performance of TS-LDPC codes due to analog impairments is negligible. Therefore, it can be concluded that the analog decoder of the TS-LDPC code using MS algorithm is fairly robust against analog imperfections and may be considered in future implementation of analog VLSI decoder.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".