Actor-Critic Deep Reinforcement Learning for 3D UAV Base Station Positioning and Delay Reduction
Bibliographic record
Abstract
In emerging network topologies such as UAVmounted base stations (UAV-BS) and ad-hoc satellite networks, optimizing the 3D-positional alignment of UAV-BS is crucial for enhancing overall network performance. Traditional approaches often rely on dedicated sensors, swarm communication, or focus on specific tasks such as mobile handover, typically assuming constant mobility. This study investigates the training of a UAVBS 3D positioning using simulated scenarios, aiming to optimize network performance metrics directly, potentially reducing the need for positional data. We extend the Actor-Critic Deep Qlearning approach, previously applied in 2D environments, to operate within a fully 3D space. Our model is trained in more challenging conditions, with sparse and mobile User Equipment (UE) and UAV-BS initialized from random positions in each training interval. The objective is to consistently minimize the UAVBS’s distance to UEs and reduce end-to-end packet transmission (Tx) delays. The resulting model shows superior performance compared to non-mobile base stations and benchmark Q-learning models across increasingly complex scenarios.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".