Minimum Broadcast Decoding Delay for Generalized Instantly Decodable Network Coding
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we introduce the concept of generalized instantly decodable network coding (G-IDNC) to further minimize decoding delay in wireless broadcast, compared to strict instantly decodable network coding (S-IDNC), studied in. G-IDNC loosens the strict instant decodability constraint in order to target more receivers while preserving the attractive properties of S-IDNC. We show that the minimum decoding delay problem for G-IDNC can be formulated as a maximum weight clique problem over a well structured graph. Since finding the maximum weight clique of a graph is NP-hard, we design a simple heuristic G-IDNC algorithm with sub-optimal performance. However, simulation results show that both proposed optimal and heuristic G-IDNC algorithms considerably outperform several other S-IDNC and G-IDNC optimal and heuristic approaches.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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