Effective Bandwidth Evaluation for VoIP Applications in IEEE 802.11 Networks
Bibliographic record
Abstract
The convergence of different classes of traffic with different priorities over the wireless network has become a reality. To insure that the users of key applications such as VoIP are satisfied with the service they receive, one must insure that the QoS criteria for these applications, such as delay, jitter or packet loss, are met This in turn means that some form of connection admission control must be used. In this paper, we show how the notion of effective bandwidth that had been previously used in wired systems can be used for CAC in WLAN IEEE 802.11b and 802.11g networks. Effective bandwidth simplifies connection admission since a new application can be accepted on a link whenever its effective bandwidth is lower than the bandwidth still available on the link. The paper presents empirical results obtained by extensive simulations showing that the admission region is nearly linear so that it is possible to design an effective linear CAC policy based solely on the effective bandwidth of a connection. We also propose a more practical method that relates to per packet effective bandwidth.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".