Performance evaluation of LDPC codes in the presence of colored noise
Bibliographic record
Abstract
The class of low density parity check (LDPC) codes includes the most powerful capacity-approaching codes reported to date. As a result, LDPC codes have been considered for application in new communication standards such as 10GBASE-T Ethernet. However, the successful use of LDPC codes for such applications requires a better understanding of the effects of signal impairments on their error correction capabilities. In this paper, the performance of various LDPC codes in the presence of additive colored Gaussian noise (ACGN) is evaluated and compared with the effects of conventional additive white Gaussian noise (AWGN). The simulation results provide insight into the performance of LDPC codes in real systems in which the noise components can indeed be correlated. In particular, our results show that at the same signal-to-noise ratio (SNR), ACGN is more detrimental to the bit error rate (BER) performance than AWGN.
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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) | 0.000 | 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".