5G New Radio Signal Propagation and Ground-to-Air Channel Modeling at 3.565 GHz Based on Extensive Measurements
Bibliographic record
Abstract
This study delves into the intricacies of 5G New Radio (NR) signal propagation, and contrary to most existing literature, focuses on the 3.565 GHz commercial frequency band through extensive ground and airborne measurements. By assessing fundamental cellular network parameters such as Channel Power (CP), Field Strength (FS), Path Loss Exponent (PLE), and Shadow Fading Amplitude (SFA), the purpose of this investigation is to gain a verified and validated insight, in alignment with the 3GPP technical report, into the highly dynamic nature of 5G NR transmissions. Encompassing both Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions at ground level, this research emphasizes realistic environments to provide precise empirical evidence on 5G signal behavior. This study further extends the current empirical knowledge base of 5G NR signal characteristics in the less-explored territory of higher-altitude signal dynamics by examining path loss and Small-Scale Fading (SSF) characteristics beyond altitudes of 120 meters, where a novel model characterizing the height dependency of PLE and fading phenomena is introduced. Notably, illustrating CP and FS for higher altitudes, this research unveils a novel and more accurate correlation between the Rician K-factor and altitude, demonstrating an increase with greater height. Significant findings from this research suggest that within the altitude range of 300–500 meters, the signal exhibits remarkable strength and stability, thus identifying this zone as ideal for capturing high-quality signals. These insights are pivotal in terms of their application in enhancing 5G cellular network coverage strategies and providing a reliable foundation for Ground-to-Air (G2A) channel models essential for Uncrewed Aerial Vehicle (UAV) communications.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".