Network congestion control in ad hoc IEEE 802.11 wireless LAN
Bibliographic record
Abstract
An IEEE 802.11 based ad hoc wireless LAN (WLAN) is formed by a set of wireless stations communicating directly with each other without using centralized administration. Ad hoc WLANs are prone to network congestion due to the bursty nature of the data traffic, synchronization difficulties in self-coordination, and the dynamics of the wireless channel. Therefore, wireless stations may experience low throughput and long latency under the circumstance of network congestion, which is especially harmful for real-time traffic. In this paper, a set of preliminary simulations and analysis are conducted to obtain a thorough understanding on the origin of network congestion. We then discuss how to capture the syndrome of network congestion. Note that the occupation rate on buffer, which is employed in active queue management (AQM) algorithms such as random early drop (RED) to predict network congestion, is not appropriate in ad hoc WLANs. New mechanisms suitable for a distributed and contention-based wireless networks are needed. We provide some alternative new designs. We use the optimized network (OPNET) simulator to evaluate the performance of the proposed algorithms and assess their ability in terms of supporting the QoS level set by the applications.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 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".