Scheduling for VoLTE: Resource Allocation Optimization and\n Low-Complexity Algorithms
Bibliographic record
Abstract
We consider scheduling and resource allocation in long-term evolution (LTE)\nnetworks across voice over LTE (VoLTE) and best-effort data users. The\ndifference between these two is that VoLTE users get scheduling priority to\nreceive their required quality of service. As we show, strict priority causes\ndata services to suffer. We propose new scheduling and resource allocation\nalgorithms to maximize the sum- or proportional fair (PF) throughout amongst\ndata users while meeting VoLTE demands. Essentially, we use VoLTE as an example\napplication with both a guaranteed bit-rate and strict application-specific\nrequirements. We first formulate and solve the frame-level optimization problem\nfor throughput maximization; however, this leads to an integer problem coupled\nacross the LTE transmission time intervals (TTIs). We then propose a TTI-level\nproblem to decouple scheduling across TTIs. Finally, we propose a heuristic,\nwith extremely low complexity. The formulations illustrate the detail required\nto realize resource allocation in an implemented standard. Numerical results\nshow that the performance of the TTI-level scheme is very close to that of the\nframe-level upper bound. Similarly, the heuristic scheme works well compared to\nTTI-level optimization and a baseline scheduling algorithm. Finally, we show\nthat our PF optimization retains the high fairness index characterizing\nPF-scheduling.\n
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".