Joint Trajectory Optimization and Time Slot Allocation for Buffer-Aided UAV Mobile Relaying
Bibliographic record
Abstract
Unmanned aerial vehicle (UAV) communication has been attracting increasing research interests recently. In this work, we study a buffer-aided single-UAV mobile relaying system which assists communication between a source node and a destination node on the ground. Specifically, in a slotted time system where each time slot experiences quasi-static channel condition, the buffer-aided UAV relay’s flight trajectory and the allocation of the time slots for transmission and reception are jointly optimized, subject to the information causality and UAV mobility constraints. The formulated problem is non-convex and the two sets of design variables, i.e., the trajectory position variables and the time slot allocation variables, are coupled. In order to make the problem tractable, we relax and decompose the original problem into two subproblems, i.e., the flight trajectory optimization subproblem and the time slot allocation subproblem, such that the two sets of design variables are decoupled. The two subproblems are optimized in an alternating manner until convergence to obtain solution to the joint optimization problem. Simulation results show that the proposed iterative alternating optimization algorithm is efficient and fast converging.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".