User Behavior-Aware Scheduling Based on Time–Frequency Resource Conversion
Why this work is in the frame
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Bibliographic record
Abstract
Time-frequency resource conversion (TFRC) is a recently proposed network resource allocation strategy. By exploiting user behavior, it withdraws and reutilizes spectrum resources strategically from connection(s) not being focused on by the user to relieve network congestion effectively. In this paper, we study downlink scheduling based on TFRC for a Long-Term Evolution (LTE)-type cellular network to maximize service delivery. The service scheduling of interest is formulated as a joint request, channel and slot allocation problem, which is NP-hard. A deflation and sequential fixing based algorithm with only polynomial-time complexity is proposed to solve the problem. For practical implementation, we propose TFRC-enabled low-complexity yet online scheduling algorithms, which integrate prediction-based leaky bucket-like traffic shaping and modified Smith ratio or exponential capacity based utility function. Furthermore, by establishing a charging model for the relationship between TFRC-enabled scheduling and its TFRC-disabled counterpart, we analytically study the benefits of integrating TFRC with scheduling. Simulation results not only verify the analysis of impact of key parameters on the performance improvement but corroborate the benefits of integrating TFRC with scheduling techniques in terms of quality-of-service provisioning and resource utilization as well.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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