Spinal Codes Over Fading Channel: Error Probability Analysis and Encoding Structure Improvement
Bibliographic record
Abstract
In order to facilitate the reliability of data transmission of Spinal codes over the fading channel, performance analysis of Spinal codes is conducted, and an improved encoding structure is proposed. First, we derive an approximate frame error rate (FER) upper bound for Spinal codes over the Rayleigh fading channel in the finite block length (FBL) regime. Then, inspired by the FER analysis process, we propose an improved encoding structure, named self-concatenation structure, to reduce the FER of Spinal codes. In addition, a parallel structure is proposed for Spinal codes to improve the decoding throughput. For the self-concatenation structure, simulation results show that it exhibits a significant gain in anti-noise performance compared with the original Spinal codes over the Rayleigh fading channel. For the parallel structure, we find that by combining the parallel structure with the self-concatenation structure, not only is the encoding and decoding throughput of Spinal codes significantly improved but also the FER of Spinal codes is reduced.
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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 | 0.001 | 0.006 |
| 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 | 0.001 | 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".