Path Planning for Cellular-connected UAV Using Parabolic Equation-based Radio Wave Propagation Models
Bibliographic record
Abstract
The high flexibility and mobility of cellular-connected unmanned aerial vehicles (UAVs) make them an important component of future wireless communication systems. However, planning their flight path is a challenging task, due to weak coverage regions in the sky and limited flight time. In this paper, we propose a novel approach that combines site-specific radio wave propagation models and deep reinforcement learning techniques to optimize the flight path for UAVs. Our approach aims to minimize the flying time while ensuring reliable communication connectivity with base stations. To that end, a parabolic equation method is employed to model radio wave propagation, while deep Q networks are applied to determine the optimal path. The proposed approach takes into account physical environmental factors and eliminates the need for time-consuming and labor-intensive measurement campaigns. We present numerical results for various random initial flying points, demonstrating the effectiveness of our approach in path planning for cellular-connected UAVs.
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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 | 0.000 | 0.001 |
| 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".