Joint Optimization of UAV Trajectory and Resource Allocation in Multi-UAV-Enabled Wireless Powered Communication Networks Under Max–Min Criterion
Bibliographic record
Abstract
We consider a wireless powered communication network (WPCN) where several unmanned aerial vehicles (UAVs) serve as energy transmitters and data collectors for low-powered ground devices (GDs). During the downlink phase, UAVs wirelessly transfer energy to the GDs, allowing them to harvest power for future data transmission. In the subsequent uplink phase, the GDs use the harvested energy to send information back to their corresponding UAVs. Our goal is to optimize system performance by maximizing the minimum throughput achieved among the GDs. We reformulate the original problem as a combination of three interrelated subproblems using a block coordinate descent approach: transmit power optimization of GDs, scheduling, and UAV trajectory optimization. These subproblems are solved iteratively to achieve an optimal solution. By adaptively adjusting UAV trajectories and optimizing scheduling and power distribution, our method ensures efficient energy delivery and reliable data collection. Numerical results confirm that the proposed approach significantly outperforms conventional methods, improving minimum throughput by up to 32.7% and 59.6% compared to fixed scheduling and fixed trajectory approaches, respectively. Furthermore, the adopted Lambert W-based energy harvesting model achieves up to 41% higher harvested energy compared to existing nonlinear energy harvesting models. Our findings underscore the potential of UAV-assisted wireless power transfer in boosting the efficiency and reliability of future wireless communication networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".