Efficient silence suppression and call admission control through contention-free medium access for VoIP in WiFi networks
Bibliographic record
Abstract
In view of the rapidly growing trend of migrating customers from traditional wired phones to mobile phones and then to VoIP services in the recent past, there is a tremendous demand for wireless technologies to support VoIP, specially on WiFi technologies which have already matured commercially. This has put forth great research challenges in the area of wireless VoIP. In this article we have addressed two core issues, efficient silence suppression and call admission control, in QoS provisioning for VoIP services in WiFi networks. In this connection we present a QoS-aware wireless MAC protocol called hybrid contention-free access (H-CFA) and a VoIP call admission control technique called the traffic stream admission control (TS-AC) algorithm. The H-CFA protocol is based on a novel idea that combines two contention-free wireless medium access approaches, round-robin polling and TDMA-like time slot assignment, and provides substantial multiplexing capacity gain through silence suppression of voice calls. The TS-AC algorithm ensures efficient admission control for consistent delay bound guarantees and further maximizes the capacity through exploiting the voice characteristic so that it can tolerate some level of non-consecutive packet loss. We expose the benefits of our schemes through numerical results obtained from simulations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".