Multi-objective scheduling for MUD based ad-hoc networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it