Location Optimization for Tethered Aerial Base Station Serving mmWave High Altitude UAVs
Bibliographic record
Abstract
Uncrewed Aerial Vehicle-User Equipment (UAV-UE) is integral to millimeter wave (mmWave)-based wireless cellular systems. UAV-UE at high altitudes encounter limited connectivity with terrestrial base stations. Tethered Aerial Base Stations (TABS) are viable alternatives to terrestrial base stations. Optimal placement of a TABS in a three-dimensional environment is necessary and critical to serve multiple moving UAV-UE units with reliable connectivity. In this work, we propose a contextual multi-armed bandit framework to learn the optimal TABS locations. We consider multiple UAV-UE units moving at high altitudes in an uplink mmWave setting. Under this framework, the TABS acts as a learning agent leveraging position information about served UAV- UE units to provide connectivity with minimum Signal to Noise Ratio (SNR) threshold requirements. We first compare the Upper Confidence Bound (UCB) and Thompson Sampling (TS)-based learning strategies against the traditional naive-based approach. Our simulation results show that the TS-based approach learns optimal locations with a 31% and 51% average regret-reduction ratio (ARR) over UCB and naive-based approaches, respectively. Also, the TS-based learning strategy for TABS reliably achieves the required SNR for UAV-UE units under multiple contexts, compared to a static TABS location.
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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".