Multi-Drone 3-D Trajectory Planning and Scheduling in Drone-Assisted Radio Access Networks
Bibliographic record
Abstract
A drone base station (DBS) is a promising technique to extend wireless connections for uncovered users of terrestrial radio access networks (RANs). To improve user fairness and network performance, in this paper, we design three-dimensional (3-D) trajectories of multiple DBSs in the drone-assisted RANs, where DBSs fly over associated areas of interests (AoIs) and relay communications between the base station and users in AoIs. We formulate the multi-DBS 3-D trajectory planning and scheduling as a mixed-integer non-linear programming (MINLP) problem with the objective of minimizing the average DBS-to-user (D2U) pathloss. The 3-D trajectory variations in both horizontal and vertical directions, as well as the state-of-the-art DBS-related channel models, are considered in the formulation. To address the non-convexity and NP-hardness of the MINLP problem, we first decouple it into multiple integer linear programming and quasi-convex sub-problems in which AoI association, D2U communication scheduling, horizontal trajectories, and flying heights of DBSs are, respectively, optimized. Then, we design a multi-DBS 3-D trajectory planning and scheduling algorithm to solve the sub-problems iteratively based on the block coordinate descent method. A k-means-based initial trajectory generation and a search-based start slot scheduling are considered in the proposed algorithm to improve trajectory design performance and ensure the inter-DBS distance constraint, respectively. Extensive simulations are conducted to investigate the impacts of DBS quantity, horizontal speed, and initial trajectory on the trajectory planning results. Compared with the static DBS deployment, the proposed trajectory planning can achieve 10-15 dB reduction on average D2U pathloss and reduce the D2U pathloss standard deviation by 68%, which indicate the improvements of network performance and user fairness.
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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 | 0.001 |
| 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) | 0.001 | 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".