Streaming Codes With Partial Recovery Over Channels With Burst and Isolated Erasures
Bibliographic record
Abstract
We study forward error correction codes for low-delay, real-time streaming communication over packet erasure channels. Our encoder operates on a stream of source packets in a sequential fashion, and the decoder must output each packet in the source stream within a fixed delay. We consider a class of practical channel models with correlated erasures and introduce new “streaming codes” for efficient error correction over these channels. For our analysis, we propose a simplified class of erasure channels that introduce both burst and isolated erasures within the same decoding window. We demonstrate that the previously proposed streaming codes can lead to significant number of packet losses over such channels. Our proposed constructions involve a layered coding approach, where a burst-erasure code is first constructed, and additional layers of parity-checks are concatenated to recover from the isolated erasure patterns. We also introduce another construction that requires a significantly smaller field-size and decoding complexity, but incurs some performance loss. Numerical simulations over the Gilbert-Elliott and Fritchman channel models indicate that by addressing patterns involving both burst and isolated erasures within the same window, our proposed codes achieve significant gains over previously proposed streaming codes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".