Computing the Asymptotic Expected Multiplicity of Elementary Trapping Sets (ETSs) in Random LDPC Code Ensembles
Bibliographic record
Abstract
The performance of low-density parity-check (LDPC) codes in the error floor region is closely related to some substructures of the code's Tanner graple (simple) cycle have a nonzero finite average multiplicity. In this paper, we compute the asymptotic expected multiplicity of such ETS structures in random LDPC code ensembles. The computation, in general, involves two subproblems: (i) counting the number of cycles of a certain length with different combinations of variable node degrees, and (ii) counting the number of ways trees of different sizes can be appended to such a cycle to form an ETS. The first subproblem, which counts the number of leafless ETSs (LETSs), is solved by the authors in other papers. The second subproblem is formulated as a recursive counting problem and is solved in this paper, with the solution being a generalization of Catalan numbers. We also demonstrate through numerical results that the asymptotic expected values computed in this paper match the multiplicity of ETSs in randomly selected finite-length LDPC codes, even at relatively short block lengths.
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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 | 0.014 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".