Opportunistic network and erasure coding for asynchronous two-way relay networks
Bibliographic record
Abstract
When deploying network coding in a two-way data exchange via a relay, time asynchronism is a practical concern requiring special treatment. If two terminal nodes generate traffic flows with the same average rate and random arrival times, in order to use network coding based on XOR-ing of packets at the relay, there is a need to buffer the data which may lead to prohibitive delays. In this paper, to bound these delays, we propose to limit the number of packets that can be buffered at the relay by periodic flushing of the buffer. When times arise that there is/are no matched packet(s) for network coding at the relay and “single packet” broadcast(s) appears unavoidable, these opportunistic transmissions are used to send erasure coded packets to improve the reliability of the data exchange. In particular, three approaches which bound the delay at the relay before sending the erasure coded packets are investigated. The approaches are to either impose a time limit for buffering the packets, limit the number of network coded transmissions made before flushing the buffer or to flush the buffer after a specific number of packets have been received from any one source. Performance tradeoffs between erasure based improvements in Packet Loss Rates (PLRs), delays, energy conservation and throughput are documented for traffic with Poisson arrival times.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".