Delay optimization in multi-hop wireless networks with network coding
Bibliographic record
Abstract
Network coding (NC) has been shown to improve the throughput of multi-hop wireless networks (MWN). Prior work on performance modeling of NC mainly addresses the maximization of throughput. However, these works fail to capture the complete picture since there may be paths in the network for which end-to-end packet delay is prohibitively high. In this paper, we address the problem of delay minimization in MWNs with NC. The objective has been assignment of wireless node capacities in a way that the average packet delay is minimized for a given network topology and the traffic demand matrix. We develop a performance analysis of the system, which models network nodes as M/G/1 queues and takes into account wireless interference. The proposed model is valid both with and without opportunistic listening for any wireless network topology. The model also incorporates network coding-aware routing that routes the flows in a manner that increases coding opportunities. We present numerical results, which show that NC reduces the average packet delay in the network and extends the stable operating region of the network. We also present simulation results, which confirm the accuracy of the analysis.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".