How Expensive is Consistency? Performance Analysis of Consistent Rate Provisioning to Mobile Users in Cellular Networks
Bibliographic record
Abstract
Providing a consistent data rate to mobile users will be a very important feature of next generation systems, i.e., 5G, especially for services such as live video-streaming, online gaming, etc. This could lead to an increased user satisfaction with these services. In this paper, we perform the analysis to determine the maximum consistent data rate that can be offered to a (high paying) class of mobile users, both within a cell and within a region covered with multiple cells, given certain available resources. We do this for two cases: 1) when the number of active users in the class is constant, and 2) for a varying number of users being simultaneously present and active in the class. The analysis is performed under some independence assumptions, but we validate our results with extensive realistic simulations where the assumptions are relaxed. We show that providing consistent rate is rather expensive because a large percentage of the available resources remain unused most of the time. However, the unused resources can be shared (possibly equally) by the users in the group. In that case the consistent rate can be seen as a guaranteed minimum rate. The other option is to allocate the unused resources to a class of best effort users. We show that using any of these options will result in significant performance improvements.
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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 | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".