Stationary Transformation of Video Traffic in LTE Networks
Bibliographic record
Abstract
In recent years, digital communication and telecommunication have gained immense usage and introduced several innovative services (e.g. video conferencing, online banking, e-Health, etc) that change the life of people. By 2020, some telecommunication providers expect to have 50 Billion devices connected to each others. The whole world is getting closely connected with the new telecommunication technologies especially the Mobile broadband technology. The expectations indicate that not only human will be connected but also devices and machines will be connected communicating altogether through the Mobile broadband technology. These tremendous improvements in telecom will introduce new and innovative services that touch every small detail of people's life. Consequently, telecom networks will become more complex, involve more resources, and demand more requirements. Thus, network optimization will be crucial to avoid wasting the resources and ensure service availability and network efficiency. To help achieving that, network operators require intelligent methods to understand the ongoing traffic and services characteristics in order to control and forecast the live/ongoing traffic. In LTE networks, video traffic makes up the largest segment of data traffic. This paper discusses the video traffic characteristics in LTE networks, checks the stationarity status of the LTE video traffic and presents a method to transform the non-stationary LTE video traffic to stationary data.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".