Time-Frequency Resource Conversion Based Scheduling for On-Demand Data Services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Time-frequency resource conversion (TFRC) is a recently proposed network resource allocation strategy. By exploiting user behavior, it withdraws spectrum resources strategically from connection(s) not focused on by the user, to relieve network congestion effectively. Aiming at supporting the exponentially increasing traffic volume, especially on-demand data services, in this work we propose TFRC-based scheduling techniques. Considering an LTE-type cellular network, we formulate the problem of service scheduling as a joint request, channel, and slot allocation problem, which is a mixed integer nonlinear programming (MINLP) problem. A deflation and sequential fixing based algorithm with only polynomial-time complexity is proposed to solve the MINLP problem. Simulation results not only demonstrate the efficiency of the proposed algorithm in terms of quality-of-service (QoS) provisioning and network resource utilization, but also show the effectiveness of the proposed TFRC-based scheduling techniques when integrating with the existing scheduling strategies such as first in first served (FIFS) and earliest deadline first (EDF).
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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 | 0.001 |
| Open science | 0.003 | 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