VoIP Capacity Allocation Using an Adaptive Voice Packetization Server in IEEE 802.11 WLANs
Bibliographic record
Abstract
WLAN VoIP capacity is known to be very low due to the effects of overheads at various protocol layers. An IEEE 802.11b access point (AP) operating at 11 Mbps for example, can support only about 12 G.711 voice connections with a 20 ms packetization interval. These effects can be mitigated by taking into account the available latency margin of the call and using it in the VoIP parameter selection. In this paper we propose the use of an adaptive voice packetization server (AVP-RTS) which splits the RTP VoIP connection into two legs. In this way each end of the call is negotiated separately and the server can allocate the available latency margin (and the ensuing capacity gain) asymmetrically across the call. We propose new algorithms for performing this capacity assignment and compare them to the conventional voice packetization scheme. Results from extensive simulations show that by using the AVP-RTS server we can significantly improve the multi-AP VoIP capacity for certain typical IEEE 802.11 situations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".