Pareto optimal resource management for wireless mesh networks with QoS assurance: Joint node clustering and subcarrier allocation
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
Node clustering and subcarrier allocation are imperative to ameliorate system throughput and facilitate quality-of-service (QoS) provisioning by means of effective interference control and maximum frequency reuse. In this paper, we propose a novel node clustering algorithm with effective tax-based subcarrier allocation tailored for wireless mesh networks with QoS support. With increased frequency reuse, our proposed approach is shown to achieve a higher system throughput than a conflict-graph approach and a baseline approach. Also, our approach is demonstrated promising in balancing packet delay and end-to-end transmission rate. By carefully adjusting an upper bound of subcarriers allocated to each cluster, we can achieve improved system performance. The proposed resource allocation achieves the Pareto optimality, demonstrating efficient use of network resources. Further, our analysis reveals that how to allocate resources in a wireless network in a decentralized manner can affect the solution space of a performance tradeoff between QoS provisioning and throughput maximization.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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