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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".