An efficient Soliton-like network coding protocol for the resource-constrained Y-network
Bibliographic record
Abstract
Originally designed for point-to-point transmissions, fountain codes are capacity-achieving codes over a binary erasure channel. On the other hand, network coding is an optimal multihop data dissemination protocol whose high decoding complexity makes it too expensive for resource-constrained applications. Soliton-like rateless coding (SLRC) has previously combined network and fountain coding paradigms such that a less complex decoder can be applied. Specifically, the coding done at an intermediate relay node allows a belief propagation decoder to be efficiently applied. We extend the SLRC protocol and propose the Improved Soliton-like Rateless Coding (ISLRC) protocol. In ISLRC, the relay applies intelligent coding and shapes the degree distribution such that performance is better than SLRC. In addition, ISLRCs performance gains are achieved using fewer resources than SLRC while maintaining all the key properties of SLRC. Simulation results show that even under the worst-case scenario of ISLRC, better performance can be achieved compared to SLRC and other existing schemes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".