Minimum delay scheduling with multi-packet transmission in wireless networks
Bibliographic record
Abstract
This paper studies the problem of minimum delay scheduling in wireless networks with multi-packet transmission capability. Specifically, we assume that the network employs superposition coding at the physical layer in order to implement multi-packet transmission. While most studies on superposition coding assume that unbounded number of packets can be coded together, physical and MAC layer limitations restrict the number of concurrent packets in a transmission set. Taking this constraint into consideration, we formulate the minimum delay scheduling as a combinatorial optimization problem and study its computational complexity under different transmission set sizes. We show that, when the transmission set size is limited to 2 packets, the problem can be solved optimally in polynomial time. Moreover, while the complexity of the problem for larger transmission set sizes is unknown, we present close-to-optimal heuristic algorithms that compute efficient solutions for the problem in polynomial time. Numerical results are also presented to study the efficiency and utility of the presented scheduling algorithms. Our results show that the heuristic algorithms are highly efficient, achieving delays that are less than 2% away from the optimal values.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".