Impact of varying UAV traffic on terrestrial users in 5G cellular network
Bibliographic record
Abstract
Next-generation cellular networks present an opportunity to enable beyond visual line of sight (BVLoS) connectivity to unmanned aerial vehicles (UAVs). This work envisions and analyzes a cellular ecosystem where UAVs can coexist with terrestrial users (TUs). This article presents a proof-of-concept system-level simulations setup to understand the impact of UAV traffic on TUs. In the simulation setup, a test network is emulated in MATLAB based upon a Tier-1 cellular network in Canada. It is assumed that the existing down-tilted antennas optimized for TUs would serve the UAV traffic from their sidelobes. An urban scenario with inter-site distance (ISD) of 700 meters is implemented and the impact on TUs is studied by varying UAV traffic per sector, UAV locations, UAVs flying height and cellular frequency. This study is conducted on a lower band at 600 MHz and mid band at 2500 MHz at three UAV altitudes 120,80 and 60 meters. Simulation results reveal that in urban areas, the average SINR at user terminals is reduced by 3.97 -to-5.25dB for 50 TUs when there are 4 to 5 UAVs in the surrounding sectors (7 cells) as compared to when no UAVs are present. But still, the available signal-to-interference and noise ratio (SINR) support reasonably good terrestrial services.
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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 | 0.001 |
| 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".