Systematic network coding for transmission over two-hop lossy links
Bibliographic record
Abstract
Packet transmission over two-hop lossy link is increasingly important in communication networks. In this paper, we present a systematic network coding scheme for packet-level transmissions over two-hop lossy links. In the scheme, a source node sends out uncoded packets in their original order first, followed by a potentially unlimited number of coded packets using random linear network coding. The intermediate node forwards a packet if it receives an uncoded packet, and sends a coded packet from previously buffered packets using random linear network coding if it does not receive a packet or the received packet is coded. We show that, compared to the scheme in which random linear network coding is used all the time at the source and intermediate nodes, the proposed method requires much less computation in encoding and decoding and also achieves a higher end-to-end rate. The benefit is appreciable when the number of source packets is not large and the finite field in which network coding is performed is small. To analytically assess the performance, we employ a Markov chain based technique to calculate the expected completion time of the proposed scheme given the number of source packets, link erasure rates and finite field size.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".