WSN02-2: Adaptive Network Resource Management in IEEE 802.11 Wireless Random Access MAC
Bibliographic record
Abstract
Effective and efficient management of wireless network resources is attracting more and more research attention, due to the rapid growing deployment of wireless mesh and ad hoc networks and to the increasing demand for Quality of Service (QoS) support in these networks. This paper proposes an adaptive network resource management scheme in the popular IEEE 802.11 random access MAC by adaptively adjusting the minimum contention window sizes of traffic flows. First, a novel generalized processor sharing (GPS) model is presented for the IEEE 802.11 random access MAC revealing the relationship between the minimum contention window size of a traffic flow and the amount of network resource this flow can receive. Using this GPS MAC model, a feedback control system model for the proposed adaptive network resource management system is developed, by directly extending our previous work in wireline GPS networks. Based on the feedback control system model, adaptive P and adaptive PI controllers are designed, and their performances are studied in simulations. Simulation results show that by using the designed controllers, the proposed adaptive network resource management approach is able to provide guaranteed distinct QoS support to traffic flows.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".