Spatially Coupled Codes via Partial and Recursive Superposition for Industrial IoT With High Trustworthiness
Bibliographic record
Abstract
For industrial Internet of Things (IIoT), data trustworthiness should be maintained both at the time of sensing and at the time of transmission. This article is concerned with trustworthiness during transmission, which is determined by transmission reliability. We present a low-complexity and flexible method via partial and recursive superposition to improve the transmission reliability of IIoT, resulting in an IIoT with high trustworthiness. In our method, a portion of the previously transmitted data are superimposed onto the current transmitted data to introduce memory among different transmissions, which are then exploited by the windowed decoder to obtain performance gain. The proposed method is referred to as partially recursive block Markov superposition transmission of low-density parity-check (PrBMST-LDPC) codes. This article is focused on the construction of low-complexity PrBMST-LDPC codes since IIoT is resource-limited in nature. The first construction is the memory-one PrBMST-LDPC code. We present a simplified density evolution algorithm to optimize the superposition ratio for memory-one PrBMST-LDPC code. Both the analytical and numerical results show that PrBMST with memory one can be used to reduce the packet loss ratio (PLR) of IIoT using LDPC codes. Particularly, around 1.0 dB performance gain is obtained by PrBMST. We then present a low-complexity construction for PrBMST-LDPC codes with encoding memory larger than one. Simulation results show that compared with memory-one PrBMST, a further PLR reduction of around one order of magnitude can be obtained.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| 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".