An Indoor Experimental Testbed for 5G-Based Uav Control and Communication
Bibliographic record
Abstract
The integration of Unmanned Aerial Vehicles (UAVs) into next-generation mobile networks is widely recognized as a key enabler of disruptive applications, where aerial platforms may function either as network nodes or as advanced network users supporting a variety of services. Unfortunately, experimental testbeds in which UAVs perform tasks while communicating with ground infrastructure over Fifth-Generation (5 G) networks remain scarce, primarily due to the challenges posed by legal restrictions on Beyond Line-of-Sight (BLoS) and autonomous operations. Motivated by this need, this work presents the design, implementation, and evaluation of a novel indoor experimental testbed for assessing the performance of UAV-based systems operating over 5 G networks. The testbed features an autonomously controlled quadcopter equipped with a 5G modem, connected to a private 5 G network implemented using SoftwareDefined Radio (SDR) technology and the OpenAirInterface (OAI) framework. To ensure a controlled environment, a motion capture system is used to provide absolute indoor positioning data, emulating Global Navigation Satellite System (GNSS) coordinates without relying on external satellites. A preliminary experimental campaign is conducted to evaluate the proposed system in terms of 5 G network performance, radio link characteristics, and UAV platform energy consumption.
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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".