Performance analysis of ad hoc wireless LANs for real-time traffic
Bibliographic record
Abstract
Compelling features of wireless local area networks (WLANs), put a variety of wireless service demands in place. In order to adjust system parameters to fulfill specific needs of different applications, a mathematical description of the system turns to be helpful. The inherent complexity of the wireless access, makes this description very challenging. We propose a new performance model for the IEEE 802.11 WLAN in ad hoc mode. The ad hoc mode has been chosen since we eventually aim at interconnected WLAN clusters where no base station exists. The model is based on the presentation of the system with a pair of one-dimensional state diagrams which can easily accommodate variations of many input parameters. The corresponding state variables are contention window size and buffer occupancy of each user in the system. The input parameters considered are: packet fragmentation factor, buffer size, and maximum allowable number of retransmissions. However, the approach taken is capable of ingesting many other probable parameters of interest. System performance criteria under study are: throughput, delay, and probability of fail to deliver. The last two are crucial for real-time 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".