Cross-layer design for multihop MANETs utility optimization with AQM
Bibliographic record
Abstract
We study the problem of jointly solving the contention and congestion distributed control problem in a bounded queue multihop mobile ad-hoc networks. Unlike the majority of the published work in this area, we focus on the feasibility of the proposed solution in case of random static and dynamic networks considering the signaling and overheads. In recent years a number of papers have presented solutions to this problem that are based on network utility maximization algorithms. However, this work typically necessitates either complex computations, heavy signaling/control overhead, and/or approximated suboptimal results. In this paper, we combine a specific network utility maximization problem with a simple and efficient Active Queue Management (AQM) mechanism that we believe is appropriate for mobile ad-hoc networks. Using IEEE 802.11 protocol as MAC layer protocol, we show via NS-3 simulations that the proposed Cross-Layer Design (CLD) significantly outperforms standard protocols such as TFRC in static and dynamic networks.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".