On the Effects of Colored Noise on the Performance of LDPC Codes
Bibliographic record
Abstract
The class of low-density parity-check (LDPC) codes includes some of the most powerful capacity-approaching codes reported to date. As a result, LDPC codes have been considered for many new communication applications. However, a better understanding of the effects of the signal impairments that exist in such applications is required. In this paper, the performance of various LDPC codes, including recent candidate LDPC codes for 10GBASE-T Ethernet, in the presence of colored noise is evaluated and compared with the effects of conventional additive white Gaussian noise (AWGN). The colored noise models in this study include high-frequency and low-frequency additive colored Gaussian noise (ACGN), and 1/f noise. The results show that LDPC codes are more vulnerable to colored noise than to AWGN and as the correlation between noise samples becomes stronger, their performance becomes more degraded. However, at the same level of colored noise power, the performance is increasingly degraded as noise correlation is spread over more noise samples
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".