Trajectory Design for the Aerial Base Stations to Improve Cellular Network Performance
Bibliographic record
Abstract
Aerial base stations (ABSs) have shown significant potential to improve cellular networks' performance due to their high mobility and on-demand deployment. In this paper, the trajectory optimization problem for a multi-ABS network is investigated. The objective is to maximize the minimum data rate of the cell-edge mobile users while the power constraint of the ABSs, including both propulsion and signal transmission powers, the backhaul link capacity constraint, and the collision avoidance constraint, are taken into account. To reach this goal, first, based on the modified K-means approach, the ABSs and users are partitioned into different clusters so that their associated ABS serve the users of each cluster. Afterward, the ABSs need to find an efficient trajectory optimization and resource allocation scheme to support the users. To solve this challenging problem, we convert the main trajectory optimization and resource allocation problem into three sub-problems: power allocation sub-problem, joint ABS-user association and sub-channel assignment sub-problem, and trajectory optimization sub-problem. Then, using the successive convex approximation approach, an efficient algorithm is proposed, which iteratively solves these sub-problems. Simulation results show that the proposed algorithm converges fast and improves the network's data rate while it satisfies all the required constraints.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".