Low-Complexity Decoder of Analog Fountain Codes for Industrial Internet of Things
Bibliographic record
Abstract
In this paper, towards the ultra-reliable low-latency requirements of industrial Internet of Things (IIoT), we design a low decoding complexity ordered statistic decoder (OSD) for short analog fountain codes (S-AFCs). We first propose a concatenated decoder named soft-OSD (S-OSD) for S-AFCs, where the S-AFCs are concatenated with LDPC codes. Then, we analyze the log-likelihood ratio (LLR) output of inner decoder via the density evolution (DE), the DE results provide the theoretical guidelines to design the discarding criterion (DC) of test error patterns (TEPs) and stopping criterion (SC) to lower the complexity of S-OSD. Simulation results show that the S-OSD can achieve the same error performance with existing decoding algorithms for S-AFCs, and the complexity of S-OSD is greatly decreased, in terms of the average re-encoding number of OSD and operations number per information bit.
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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 | 0.001 | 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".