A comparison between BP, log-likelihood and max log-likelihood decoding algorithms of LDPC codes based on EXIT chart and EXIT trajectories methods
Bibliographic record
Abstract
Low density parity check (LDPC) codes are known to achieve a performance very close to the Shannon capacity limit on Additive White Gaussian Noise (AWGN) and erasure channels. The extrinsic information transfer (EXIT) chart is a powerful method for analyzing iteratively decoded codes such as Turbo codes. EXIT charts were introduced as a method for representing how the mutual information between the decoder output and the transmitted bits changes over turbo decoding iterations. It is possible to apply the EXIT chart method to LDPC codes by treating the LDPC decoder as a concatenation of variable and check nodes. However the achieved convergence threshold values obtained with the EXIT charts are (plusmn0.1) dB away from the more precise results derived from the density evolution (DE) method. Subsequently, the EXIT trajectories method was proposed as an improved performance-analysis method for LDPC codes under belief propagation (BP) decoding and achieved more accurate convergence threshold values than the EXIT chart method. In this paper, the EXIT chart and the EXIT trajectories methods are proposed as analysis tools to compare between the BP, log likelihood and max log likelihood decoding algorithms for LDPC codes in terms of convergence thresholds. Furthermore, a comparison between EXIT chart, EXIT trajectories and DE methods is introduced for the mentioned LDPC codes. Simulations and numerical calculations on convergence thresholds for various ensembles of (dv, dc) regular LDPC codes for binary input AWGN channels are performed which results that the BP is the best decoding algorithm for LDPC code followed by the log likelihood and the max log likelihood algorithms, respectively. Also, the EXIT trajectory method yields more accurate results than EXIT charts which come very close to the DE method, while being more convenient numerically as a code research tool than the latter which require many Fourier transform operations.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".