QoS-Aware Energy-Efficient Downlink Predictive Scheduler for OFDMA-Based Cellular Devices
Bibliographic record
Abstract
We propose a predictive energy-efficient scheduling scheme that optimizes the user equipment (UE)'s bits/joule metric subject to quality-of-service (QoS) constraints in downlink orthogonal frequency-division multiple access (OFDMA) systems. This is achieved by minimizing the number of wake-up transmission time intervals (TTIs), where the UE receiver circuit is on, in a longer time horizon than studied before. The proposed predictive scheduler is supported by a ray-tracing (RT) engine that increases the scheduler's knowledge by long-term information about the users' propagation characteristics. A convex multiobjective binary-integer-programming formulation for the problem is presented to optimize both the UE's energy efficiency (EE) and QoS. The multiobjective formulation is then used for benchmarking of a low-complexity and computationally efficient heuristic scheduler. The results show that the proposed schedulers have significantly improved the UE's EE and the overall capacity of the system, compared with a recently published EE scheduling scheme while maintaining target QoS.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".