DEVS-based modeling of cached and segmented video download algorithms in LTE-A cellular networks
Bibliographic record
Abstract
Cellular networks have witnessed increasing demands for higher data rates in the recent years. Satisfying these demands presents a challenge for cellular network operators. Video traffic plays a major role in this, as it accounted for more than half of the data traffic on cellular networks recently. Device-to-Device (D2D) communication, introduced by the Long Term Evolution-Advanced (LTE-A) standard, allows direct communication between User Equipments (UE) in the network. We proposed cached and segmented video download algorithms that employ D2D communication to improve the throughput of video transmission over LTE-A cellular networks. Here, we present the Modeling and Simulation (M&S) of an LTE-A network that implements the proposed algorithms. We used the Discrete Event System Specification (DEVS) formalism to model the network. Simulation results show that significant improvements are achieved by the proposed algorithms in terms of the average and aggregate data rates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".