Multiple Drone-Cell Deployment Analyses and Optimization in Drone Assisted Radio Access Networks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose a drone assisted radio access networks architecture in which drone-cells are leveraged to relay data between base stations and users. Based on the state-of-the-art drone-to-user and drone-to-base station (D2B) channel models, we first analyze the user coverage and the D2B backhaul connection features of drone-cells. We then formulate the 3-D drone-cell deployment problem with the objective of maximizing the user coverage while maintaining D2B link qualities, for a given number of drone cells being deployed. To solve the problem, the particle swarm optimization (PSO) algorithm is leveraged for its low computational cost and unique features suiting the spatial deployment of drone-cells. We propose a per-drone iterated PSO (DI-PSO) algorithm that optimizes drone-cell deployments for different drone-cell numbers, and prevents the drawbacks of the pure PSO-based algorithm derived from related works. Simulations show that the DI-PSO algorithm can achieve higher coverage ratio with less complexity comparing to the pure PSO-based algorithm.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it