Measured and RT-based A2G Channel Dataset (CIR) under Urban Scenarios
Why this work is in the frame
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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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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it