Power Efficient Trajectory Optimization for the Cellular-Connected\n Aerial Vehicles
Bibliographic record
Abstract
Aerial vehicles have recently attracted significant attention in a variety of\ncommercial and civilian applications due to their high mobility, flexible\ndeployment and cost-effectiveness. To leverage these promising features, the\naerial users have to satisfy two critical requirements: First, they have to\nmaintain a reliable communication link to the ground base stations (GBSs)\nthroughout their flights, to support command and control data flows. Second,\nthe aerial vehicles have to minimize their propulsion power consumption to\nremain functional until the end of their mission. In this paper, we study the\ntrajectory optimization problem for an aerial user flying over an area\nincluding a set of GBSs. The objective of this problem is to find the\ntrajectory of the aerial user so that the total propulsion-related power\nconsumption of the aerial user is minimized while a cellular-connectivity\nconstraint is satisfied. This problem is a non-convex mixed integer non-linear\nproblem and hence, it is challenging to find the solution. To deal with, first,\nthe problem is relaxed and reformulated to a more mathematically tractable\nform. Then, using successive convex approximation (SCA) technique, an iterative\nalgorithm is proposed to convert the problem into a sequence of convex problems\nwhich can be solved efficiently.\n
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".