Voice call quality using 802.11e on a wireless mesh network
Bibliographic record
Abstract
Wireless local area networks (WLANs) provide an affordable solution for last mile network access. They also allow for extension of a network by configuring a wireless mesh network (WMN) where it may otherwise be physically infeasible or cost prohibitive to do so. With the increasing use of real-time applications such as video conferencing and Voice over IP (VoIP), networks are stressed to guarantee QoS requirements for these applications. Examples of key requirements include bounded delay and packet loss ratios. Addressing this issue in WLANs, the IEEE 802.11e amendment was proposed to provide a QoS mechanism. However, the performance of 802.11e in meshed environments is yet to be studied. In this work, we study VoIP call quality in a meshed environment with provisions for QoS. We study the call quality and throughput of background traffic in an experimental WMN testbed in order to test how well the IEEE 802.11e QoS provisions support voice calls. Call quality is tested in different configurations and scenarios. We study the effect of the number of wireless hops on VoIP call quality. In addition, we investigate the number of VoIP calls that can be supported simultaneously for different numbers of wireless hops. We also study how fairly the network treats different calls in different configurations. Then, we look at how much effective bandwidth a VoIP call uses on the network. Finally, we examine the VoIP call quality of different calls when calls have different QoS parameters and study the effect that a busy central node has on traffic passing through it. We provide suggestions to improve call quality on a WMN and hint at possible future work.
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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 | 0.003 |
| 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 | 0.000 | 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".