Transmission Time Minimization-Based UAV Deployment and Resource Allocation With Random User Position Information
Bibliographic record
Abstract
Exploiting unmanned aerial vehicles (UAVs) in terrestrial cellular networks has received considerable attention for their advanced transmission capability, flexible deployment and cost effectiveness, etc. In certain communication scenarios where the links from ground users (GUs) to base stations (BSs) or satellites may not be accessible, UAVs can be deployed as aerial relays (ARs) to forward data packets for the GUs. In this paper, we investigate the AR deployment and resource allocation problem in a UAV-assisted satellite communication system. Stressing the importance of system transmission time, we formulate the joint AR deployment, power allocation and user association problem as a constrained system transmission time minimization problem. Since the formulated optimization problem is non-convex and non-linear with the AR deployment and user association variables being coupled, it is challenging to solve. To tackle this problem, the original optimization problem is decomposed into subproblems, namely AR deployment and power allocation subproblem, and user clustering and association subproblem. Given the initial user association strategy and the number of ARs, the AR deployment and power allocation subproblem is formulated and solved by using multi-agent deep Q network algorithm. Then, given the AR deployment and power allocation strategy, we formulate the user clustering and association subproblem and propose an improvedK-means-based user clustering and association algorithm. The two subproblems are tackled in an iterative and embedded manner. Simulation results demonstrate the effectiveness of the proposed algorithms.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".