Reliable packet transmissions in multipath routed wireless networks
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Bibliographic record
Abstract
We study the problem of using path diversification to provide low probability of packet loss (PPL) in wireless networks. Path diversification uses erasure codes and multiple paths in the network to transmit packets. The source uses Forward Error Correction (FEC) to encode each packet into multiple fragments and transmits the fragments to the destination using multiple disjoint paths. The source uses a load balancing algorithm to determine how many fragments should be transmitted on each path. The destination can reconstruct the packet if it receives a number of fragments equal to or higher than the number of fragments in the original packet. We study the load balancing algorithm in two general cases. In the first case, we assume that no knowledge of the performance along the paths is available at the source. In such a case, the source decomposes traffic uniformly among the paths; we call this case blind load balancing. We show that for low PPL, blind load balancing outperforms single-path transmission. In the second case, we assume that a feedback mechanism periodically provides the source with information about the performance along each path. With that information, the source can optimally distribute the fragments. We show how to distribute the fragments for minimized PPL, and maximized efficiency given a bound on PPL. We evaluate the performance of the scheme through numerical simulations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it