MADRL-Based Multi-UAV 3D Trajectory Planning for 6G-Oriented Communication Assistance
Bibliographic record
Abstract
Due to its unique signal propagation environment, the introduction of uncrewed aerial vehicles (UAVs) for 6G communications has brought many new challenges. These new challenges, particularly increased resource constraints and signal interference among UAVs, necessitate optimal UAV deployment through trajectory planning. Unfortunately, existing two-dimensional (2D) UAV trajectory planning techniques with pre-determined heights can hardly meet the demands of ground user equipment (UE) through opportunistic use of limited radio resources. To overcome the related issues, a new multi-UAV three-dimensional (3D) trajectory planning strategy enabled by multi-agent deep reinforcement learning (MADRL) is proposed in this work. First, the position of each UAV at every timeslot can be adaptively adjusted in 3D to boost the agility of on-demand deployment. Furthermore, more comprehensive system performance metrics, including UE coverage rate, downlink sum rate, and energy consumption, are jointly considered as the optimization objectives, formulating a multi-objective optimization problem for multi-UAV 3D trajectory planning. To support the diverse demands of UAVs autonomously, a MADRL algorithm, multi-agent proximal policy optimization (MAPPO), is further developed as the solution. Simulations have been conducted based on practical scenario settings. The results indicate that the improved MAPPO can empower the 3D movements of UAVs based on their local observations while optimizing the system performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".