Multi-objective scheduling for MUD based ad-hoc networks
Bibliographic record
Abstract
Common channel multi-hop Ad Hoc networks have some inherent constraints related to throughput and Quality of Service (QoS). Multiuser detection (MUD) based Medium Access Control (MAC) can relax some of these constraints and provide significant gains in throughput and Quality of Service (QoS). These gains can be realized by implementing a distributed neighborhood scheduling algorithm that needs to choose one from several possible transmission configurations in each frame. This feature allows formulating different scheduling performance objectives such as delay minimization or throughput maximization. In this paper we focus on analysis and comparison of the system performance under different objectives including multi-objective formulations. First we implement a scheduling scheme that minimize delay using Start Time Fair Queuing (STFQ) algorithm and compare its performance with scheduling that maximises the throughput. Then we formulate multi-objective functions that are used to achieve a trade-off between delay and throughput performance. One of these formulations is based on the Nash arbitration scheme from cooperative game theory. The numerical results demonstrate the flexibility and efficiency of the proposed approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".