Multiplexed Streaming Codes for Multi-Hop Multi-Link Communication Networks
Bibliographic record
Abstract
This paper studies the transmission of a stream of data packets through a multi-hop multi-link communication network. The source generate sequential source packets and transmits them through a network to a destination. Each intermediary node is allowed to perform any processing operation in order to successfully forward the message to the destination. Each link connecting two nodes is subject to an adversarial packet erasure channel, where the number of erasures is limited. The destination must recover each source packet within a delay deadline, otherwise that packet is considered lost. The particular case when each hop consists of only one link has been studied by Domanovitz et al. (2020). We extend this work to the multi-link scenario by multiplexing multiple copies of streaming codes in Domanovitz et al. (2020) and optimizing the rate using a linear programming framework. We also demonstrate how our framework can be naturally extended when there is a different propagation delay on each link. Numerical results demonstrate that our proposed approach provides significant improvements over baseline schemes. We further demonstrate that our proposed scheme also provides improved audio quality over baseline schemes in experiments involving Gilbert-Elliott channels.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".