Research on QC-LDPC Decoding Method With Low Quantization Word Length Based on Adaptive Information Mapping in Passive Optical Network
Bibliographic record
Abstract
Passive Optical Networks (PON) have advantages such as stable performance, convenient installation, high bandwidth, and resource saving, making them a mainstream network access technology with broad application prospects. The development of such as 8K video, digital twins, VR and other technologies causes explosive growth of network traffic and drives the next generation of PON to evolve towards higher rates, which results in the use of forward error correction (FEC) coding with higher coding gain to improve the power budget of PON. Quasi-cyclic low density parity check (QC-LDPC) codes are widely utilized in PON systems due to high coding gain and parallel encoding and decoding capabilities. However, the application of turbo-decode message passing (TDMP) decoding method is inevitably equated with high hardware complexity of the decoder caused by storing and processing massive information, which is one of the obstacles to PON evolution. Reducing the quantization word length is effective to reduce hardware complexity, but it also leads to saturation of decoding information, causing errors and affecting decoding performance. This work proposes an nonlinear mapping method which utilizes adaptive compression of decoding information through node saturation state monitoring during the decoding process, in order to ensure that the probability density of node information has a reasonable distribution. The simulation results indicate that the method can effectively mitigate the decline in decoding performance under low-word-length conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".