Energy-Efficient Network Slicing for UAV-enabled Networks
Bibliographic record
Abstract
This paper investigates an unmanned aerial vehicle (UAV)-enabled network slicing problem to provide content delivery and sensing data gathering services. Two tenants provide services to their clients by sharing a common infrastructure of a set of UAVs. The content delivery tenant needs to guarantee that each of its clients (users) receives the required content, while the sensing tenant aims to gather an adequate amount of uncorrelated data. An energy consumption minimization framework is considered to meet the tenants’ requirements by optimizing the number of deployed UAVs, the deployment location of each UAV, the transmit power of each deployed UAV, and the user-UAV association. Taking into consideration the spatial correlation among the sensing users, a subset of these users is activated to gather the required sensing information. A heuristic solution approach is presented to select the suitable deployment location of the UAVs, activate the optimum subset of the sensing users, and associate the users of both tenants with the deployed UAVs. Simulation results illustrate that the proposed framework significantly reduces the total energy consumption.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".