Optimal and Approximate Mobility Assisted Opportunistic Scheduling in Cellular Data Networks
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Bibliographic record
Abstract
This paper considers the problem of scheduling of multiple users in the downlink of a time-slotted cellular data network. It introduces optimal and approximate opportunistic scheduling algorithms, which combine channel variations and user mobility information in the decision rule. The proposed algorithms modify opportunistic scheduling algorithm of Liu et al. with dynamic fairness constraints that adapt according to the user mobility. The optimum algorithm is an offline algorithm because it pre-computes constraint values for all mobility states according to a known mobility model. The approximate algorithm is an on-line algorithm, and it relies on the future prediction of user mobility locations in time. These predicted values are used in computing constraint values. Simulation results illustrate the usefulness of the proposed schemes for elastic traffic and restrictive constraints. The use of mobility information in opportunistic scheduling also increases channel capacity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it