User Behavior-Aware Scheduling Based on Time–Frequency Resource Conversion
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".