Adaptive Relaying for Streaming Erasure Codes in a Three Node Relay Network
Bibliographic record
Abstract
This paper investigates adaptive streaming codes over a three-node relayed network. In this setting, a source node transmits a sequence of message packets to a destination with help of a relay. The source-to-relay and relay-to-destination links are unreliable and introduce at most$N_{1}$and$N_{2}$packet erasures, respectively. The destination node must recover each message packet within a strict delay constraint$T$. The paper presents a new construction of streaming codes for all feasible parameters$\{N_{1}, N_{2}, T\}$. Our work improves upon the construction in Fong et al. by adapting the relaying strategy based on the erasure patterns from source to relay. Specifically, the code employs the notion of symbol estimates, which allows the relay to forward information about symbols before it can decode that symbol, and variable-rate encoding, which decreases the rate used to encode a packet as more erasures affect that packet. The codes proposed in this paper achieve rates higher than the ones proposed by Fong et al. whenever$N_{2} > N_{1}$, and achieve the same rate when$N_{2} \leq N_{1}$, in which case the rate is optimal. The paper also presents an upper bound on the achievable rate that takes into account erasures in both links in order to bound the rate in the second link. The upper bound is shown to be tighter than a trivial bound that considers only the erasures in the second link.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".