Measured and RT-based A2G Channel Dataset (CIR) under Urban Scenarios
Bibliographic record
Abstract
Air-to-ground (A2G) channels play a pivotal role in reliable communications between drone and ground terminal. A2G channel modeling is a hot topic, however there is little measurement data in real scenarios for model validation and comparison. We have conducted channel measurements in urban scenario at the 3.6 GHz band with a bandwidth of 61.44 MHz. The antennas for both the Tx and Rx are replaced by dipole omnidirectional antennas. The street canyon is approximately 130 m in length and 15 m in width, surrounded by buildings ranging from 20 m to 44 m height. The ground Rx antenna is placed at a height of 2 m. Instrucitons can also be found in the guidemanual_Measured and RT-based Channel Dataset (CIR) under Urban Scenarios.pdf. More details about the channel sounder and dataset can be found in the following references. [1]. Kai Mao, Qiuming Zhu, et al., A Survey on Channel Sounding Technologies and Measurements for UAV-Assisted Communications. IEEE Transactions on Instrumentation and Measurement, 2024, Vol.73, pp.1-24. [2]. Kai Mao, Qiuming Zhu, Yanheng Qiu, et al., A UAV-Aided Real-Time Channel Sounder for Highly Dynamic Non-Stationary A2G Scenarios. IEEE Transactions on Instrumentation and Measurement, 2023, 72:1-15. [3]. Kai Mao, Qiuming Zhu, et al. Demo Abstract: A UAV-Based Real-Time Channel Knowledge Mapping System. IEEE International Conference on Computer Communication (INFOCOM), Vancouver, Canada, May, 2024, 1-2.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".