Improved Low-Complexity Soliton-Like Network Coding for a Resource-Limited Relay
Bibliographic record
Abstract
In this paper, we examine the marriage of Fountain coding and network coding (NC). Fountain codes are capacity achieving erasure codes designed for point-to-point transmissions. NC is a throughput-optimal data dissemination technique, but its high-complexity decoding makes it unattractive for applications where limited resources are available. In this paper, we consider Fountain network coding to take advantage of efficient fountain decoders. Protocols such as Soliton-like rateless coding (SLRC) have previously addressed this issue, yet the re-encoding at the relay is expensive while there is still room for improving the performance. Extending SLRC, we propose the Improved Soliton-like Rateless Coding (ISLRC) protocol, where the relay is designed to perform distribution shaping given limited resources. ISLRC preserves the same properties as SLRC, but also makes the aggregate degree distribution more efficient for Fountain decoding. We analyze ISLRC's degree distribution and perform an asymptotic error analysis for the case where resources are most scarce. The ISLRC scheme is compared against other existing schemes. 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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".