Pareto optimal resource management for wireless mesh networks with QoS assurance: Joint node clustering and subcarrier allocation
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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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 | 0.002 |
| 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 | 0.000 |
| 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".