Utilizing IEEE 802.11n to enhance QoS support in wireless mesh networks
Bibliographic record
Abstract
Wireless mesh networks (WMNs) have the potential in supporting multimedia applications with last-mile Internet access. To achieve this objective, shortcomings such as scarcity of the wireless link capacity and the lack of robust QoS scheduling must be overcome. In this paper, we consider WMNs utilizing the new IEEE 802.11n standard. Based on the standardpsilas physical and medium access control (MAC) layer enhancements, we propose adapting the modulation and code scheme (MCS) index and aggregation frame length according to the online assessed link quality, performing frame aggregation by packing multiple small subframes. We also propose performing QoS bandwidth provisioning by optimally aggregating subframes according to their QoS constraints and fairness. Performance results show that incorporating link adaptation, frame aggregation, and QoS bandwidth provisioning can considerably improve the system performance in terms of MAC delay, achieved throughput and packet dropping ratio. The results also show that packet aggregation scheme has a crucial impact on system performance when aggregating small packet sizes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".