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 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 | 0.001 | 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 | 0.001 | 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".