Delay Minimization in Multihop Wireless Networks: Static Scheduling Does It
Bibliographic record
Abstract
Abstract—In this paper, we address two issues in multihop wireless networks—poor end-to-end delay performance and high per-slot computational overhead of the classical max-weight algorithm. To reduce the end-to-end delay, we first propose a simple modification to the classical maximum weight scheduling algorithm that promotes the use of shorter paths by the packets. The significantly lower delays are shown via simulation. The modification that we suggest does not reduce the schedulable region and has the same complexity as the classical algorithm. Next, we propose a static routing and scheduling scheme that is obtained by adapting the classical optimal routing problem of wireline networks to multihop wireless networks. The static scheme slows the timescale of routing and scheduling computations from per-slot to the timescale of change in the network traffic pattern; thus the computation complexity is reduced. We also show, via simulations, that the delay performance in the static scheme is comparable to that of the dynamic scheme that we have proposed. I.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".