Integrated UAV Trajectory Control and Resource Allocation for UAV-Based Wireless Networks With Co-Channel Interference Management
Bibliographic record
Abstract
In this article, we study the trajectory control, subchannel assignment, and user association design for unmanned aerial vehicles (UAVs)-based wireless networks. We propose a method to optimize the max-min average rate subject to data demand constraints of ground users (GUs) where spectrum reuse and co-channel interference management are considered. The mathematical model is a mixed-integer nonlinear optimization problem which we solve by using the alternating optimization approach where we iteratively optimize the user association, subchannel assignment, and UAV trajectory control until convergence. For the subchannel assignment subproblem, we propose an iterative subchannel assignment (ISA) algorithm to obtain an efficient solution. Moreover, the successive convex approximation (SCA) is used to convexify and solve the nonconvex UAV trajectory control subproblem. Via extensive numerical studies, we illustrate the effectiveness of our proposed design considering different UAV flight periods and number of subchannels and GUs as compared with a simple heuristic.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".