Piggybacking on UAV Package Delivery Systems to Simultaneously Provide Wireless Coverage: A Deep Reinforcement Learning-Based Trajectory Design
Bibliographic record
Abstract
Various studies have explored the possibility of utilizing unmanned aerial vehicles (UAVs) as last-mile package delivery agents and aerial base stations in recent years. Despite the tremendous attention in these applications, nearly all of the studies assumed that the UAVs are serving just one purpose, but not both simultaneously; however, for a number of reasons, such as traffic congestion in the sky and energy & resource inefficiency problems, it seems more suitable for the UAVs to be serving several services concurrently. A few papers investigated the case in which the UAV acts as both a package delivery agent and a wireless transceiver, but in all of them, package delivery time constraints were never considered. Stemming from the observation that there are various ongoing industrial projects in UAV-based package delivery, we investigate the possibility of piggybacking on UAV-based package delivery infrastructures to also provide wireless coverage, and consider the problem of designing UAV trajectories that maximize the cumulative downlink sum rate of the ground communication users while simultaneously delivering packages under strict delivery time constraints. We use deep Q-learning (DQL) to solve this optimization problem, and demonstrate the successful formulation & implementation of our algorithm through several simulations.
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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 | 0.000 |
| 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".