Coding Opportunity Densification Strategies for Instantly Decodable\n Network Coding
Bibliographic record
Abstract
In this paper, we aim to identify the strategies that can maximize and\nmonotonically increase the density of the coding opportunities in instantly\ndecodable network coding (IDNC).Using the well-known graph representation of\nIDNC, first derive an expression for the exact evolution of the edge set size\nafter the transmission of any arbitrary coded packet. From the derived\nexpressions, we show that sending commonly wanted packets for all the receivers\ncan maximize the number of coding opportunities. Since guaranteeing such\nproperty in IDNC is usually impossible, this strategy does not guarantee the\nachievement of our target. Consequently, we further investigate the problem by\nderiving the expectation of the edge set size evolution after ignoring the\nidentities of the packets requested by the different receivers and considering\nonly their numbers. We then employ this expected expression to show that\nserving the maximum number of receivers having the largest numbers of missing\npackets and erasure probabilities tends to both maximize and monotonically\nincrease the expected density of coding opportunities. Simulation results\njustify our theoretical findings. Finally, we validate the importance of our\nwork through two case studies showing that our identified strategy outperforms\nthe step-by-step service maximization solution in optimizing both the IDNC\ncompletion delay and receiver goodput.\n
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".