Voice over Wi-Fi: Feasibility analysis
Bibliographic record
Abstract
Once considered only a limited mean for short-range communication of best-effort traffic, Wi-Fi's roots have speared into way farther lands. Voice-over-Wi-Fi (VoWiFi) is an alternative data-driven IP-based dialing service for indoor users with poor cellular coverage. The recent rollout of iPhone6+ running IOS8 with novel Wi-Fi dialing feature spurred several operators around the world to invest on VoWiFi as a complementary service. As such, when cellular coverage is poor, calls are placed/switched over to wireless local area network (WLAN) in a transparent manner to users. Given the heterogeneity of the setup, a high uncertainity arises regarding whether the switching (handoff) task remains unnoticed to the users or not. There is also the issue of interference over WLAN as well as congestion in internet core that is often added to the obscurities. Inspired by these facts, this paper investigates the suitability of VoWiFi in satisfying the voice service quality requirements for home and small network users. Three different architectures are investigated based on handoff, load-balancing and repeater. The voice reception quality in terms of jitter, delay and packet loss is monitored for each architecture. It is observed that delay, jitter and packet loss are improved in repeater-based scenario whereas call drop is experienced in both handoff and load balancing scenarios.
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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 | 0.003 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".