New speech traffic background simulation models for realistic VoIP network planning
Bibliographic record
Abstract
In order to overcome the known challenges of transmitting multimedia traffic over a switched packet network (i.e. latency, jitter, packet loss, etc.), careful network planning needs to take pace. Existing simulation platforms, particularly for Voice over IP (VoIP) simulations, have a limited selection of speech encoding algorithms. The primary objective of this paper is the creation of simulation models to be essential components of a simulation platform. Such a tool is aimed at supporting the planning and design phases of packet switched networks carrying voice traffic while considering realistic and current network conditions and simulation features. More specifically, the contribution of this paper is the creation of a speech background traffic generation model that generates traffic that follows statistical behaviour of a number of speech encoding algorithms. The purpose of such model is to provide relevant and current background traffic shape to VoIP simulations. To date, fix and variable data rate en coding algorithms (G.729, G.711, iLBC, Speex and AMR) are included in the codec choice for the model. Finally, the model and all its components are also made available to the scientific community [1].
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".