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arxiv: 2607.00541 · v1 · pith:YIHXJSQ7new · submitted 2026-07-01 · 📡 eess.SP

Measurement-Based Characterization and Statistical Modeling of 6G Urban Low-Altitude A2G Channels across FR1 and FR3

Bin Ao , Jianhua Zhang , Pan Tang , Peijie Liu , Boyang He , Hao Zheng This is my paper

Pith reviewed 2026-07-02 07:48 UTC · model grok-4.3

classification 📡 eess.SP
keywords A2G channelsUAV communicationspath loss modelingRMS delay spreadRician K-factorLoS/NLoS classificationurban propagation6G
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The pith

Measurements show the close-in path loss model fits urban low-altitude A2G data better than the 3GPP reference across 2.85 to 7.25 GHz.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper reports a measurement campaign of wideband air-to-ground channels in an urban setting at three frequencies spanning FR1 and FR3 bands. It introduces a weakly supervised classifier that labels LoS and NLoS states by combining geometry, channel statistics, and spatial consistency. Analysis of the labeled data indicates that the close-in model tracks measured path loss more closely than the 3GPP model, that NLoS cases produce steeper path-loss exponents and larger shadow fading, and that delay spread shrinks and the Rician K-factor rises as frequency increases under LoS conditions. The RMS delay spread is shown to follow a lognormal distribution and the K-factor a normal distribution.

Core claim

The close-in model fits the measured PL more accurately than the 3GPP reference model, and NLoS propagation leads to larger path loss exponents and stronger SF than LoS propagation. For channel delay characteristics, higher-frequency channels exhibit fewer effective MPCs and weaker delay dispersion. Specifically, the mean RMS-DS under LoS conditions decreases from 93.11 to 46.84 ns, while the mean Rician K-factor increases from 9.16 to 12.88 dB. The statistical results further show that the RMS-DS and the Rician K-factor can be well characterized by lognormal and normal distributions, respectively.

What carries the argument

Wideband A2G channel measurements at 250 MHz bandwidth combined with a weakly supervised LoS/NLoS identification method that fuses geometric priors, channel features, and spatial consistency constraints.

If this is right

  • Link-budget calculations for 6G UAV systems should adopt the close-in model rather than the 3GPP reference for the tested frequency range.
  • System designers must allocate extra margin for larger shadow fading and steeper exponents when NLoS conditions dominate.
  • Equalizer and resource-allocation algorithms can exploit the observed increase in channel sparsity at higher carrier frequencies.
  • Monte-Carlo simulators of A2G links can draw RMS-DS from lognormal and K-factor from normal distributions fitted to the measured parameters.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same weakly supervised labeling pipeline could be applied to mmWave or sub-THz A2G campaigns to test whether the sparsity trend continues.
  • Receiver trajectories that induce the reported spatial non-stationarity could be used to benchmark time-varying channel models required for mobile UAV handoff.
  • The measured frequency dependence of RMS-DS and K-factor supplies concrete targets for calibrating geometry-based stochastic channel generators.

Load-bearing premise

The weakly supervised labeling method produces sufficiently accurate LoS and NLoS assignments that the reported differences in path loss, delay spread, and K-factor are not caused by classification errors.

What would settle it

Re-labeling the same raw measurement traces with an independent method such as synchronized video or deterministic ray tracing and finding that the path-loss-exponent gap or the RMS-DS frequency trend reverses or disappears.

Figures

Figures reproduced from arXiv: 2607.00541 by Bin Ao, Boyang He, Hao Zheng, Jianhua Zhang, Pan Tang, Peijie Liu.

Figure 1
Figure 1. Figure 1: Overview of the A2G channel measurement system: (a) Block diagram [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Flowchart of the proposed weakly supervised LoS/NLoS identification [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 2
Figure 2. Figure 2: A2G channel measurement scenario: (a) satellite view of the [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Reconstructed 3D urban scenario with identified LoS/NLoS states [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: LoS/NLoS identification accuracy comparison among geometry-only [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Measured power delay profiles at different frequency bands: (a) 2.85 GHz; (b) 4.6 GHz; (c) 7.25 GHz. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: PDFs of the number of MPCs across different frequency bands: (a) [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Measured path loss, CI model, FSPL, and 3GPP TR 36.777 UMa-AV model fitting at three frequency bands under different propagation conditions: [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 10
Figure 10. Figure 10: CDFs and normal fitting curves of the Rician [PITH_FULL_IMAGE:figures/full_fig_p010_10.png] view at source ↗
read the original abstract

Unmanned aerial vehicle (UAV) communications have been recognized as a key component of future sixth-generation (6G) space-air-ground-sea integrated networks. Accurate characterization and modeling of air-to-ground (A2G) channels are essential for the design and optimization of low-altitude communication systems. This paper presents a wideband A2G channel measurement campaign in an urban environment at 2.85 and 4.6~GHz in FR1 and 7.25~GHz in the FR3 frequency band, each with a bandwidth of 250~MHz. To enable reliable line-of-sight (LoS) and non-line-of-sight (NLoS) propagation state identification, a weakly supervised method is developed by fusing geometric priors, channel features, and spatial consistency constraints. Furthermore, based on the measured data, A2G channel characteristics are extracted and analyzed under LoS/NLoS conditions across different frequency bands, including path loss (PL), shadow fading (SF), power delay profile, root-mean-square delay spread (RMS-DS), and Rician $K$-factor. The results show that the close-in model fits the measured PL more accurately than the 3GPP reference model, and that NLoS propagation leads to larger path loss exponents and stronger SF than LoS propagation. For channel delay characteristics, higher-frequency channels exhibit fewer effective MPCs and weaker delay dispersion, indicating increased channel sparsity. Specifically, the mean RMS-DS under LoS conditions decreases from 93.11 to 46.84~ns, while the mean Rician $K$-factor increases from 9.16 to 12.88~dB. The statistical results further show that the RMS-DS and the Rician $K$-factor can be well characterized by lognormal and normal distributions, respectively. Moreover, the movement of the receiver in a complex scattering environment intensifies the spatial non-stationarity of the A2G channel.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript reports a wideband A2G channel measurement campaign in an urban environment at 2.85 GHz, 4.6 GHz (FR1), and 7.25 GHz (FR3), each with 250 MHz bandwidth. It introduces a weakly supervised LoS/NLoS classification method that fuses geometric priors, channel features, and spatial consistency constraints, then extracts and statistically models path loss (comparing close-in vs. 3GPP), shadow fading, RMS delay spread, and Rician K-factor under LoS/NLoS conditions. Key reported results include superior close-in model fit, larger NLoS path-loss exponents and shadow fading, frequency-dependent reduction in mean RMS-DS (93.11 ns to 46.84 ns under LoS) and increase in mean K-factor (9.16 dB to 12.88 dB), lognormal and normal distributional fits for RMS-DS and K-factor, and increased spatial non-stationarity with receiver motion.

Significance. If the LoS/NLoS labels prove reliable, the work supplies concrete empirical statistics across FR1 and FR3 bands for low-altitude UAV channels, including explicit parameter values and distribution fits that can directly inform 6G A2G link budgets and simulators. The measurement-based nature with reported numerical values (rather than purely simulated) is a positive attribute for model validation.

major comments (1)
  1. [Method for LoS/NLoS identification] Description of the weakly supervised LoS/NLoS method: no accuracy metric, precision/recall, agreement with ray-tracing, manual labels, or sensitivity analysis is reported. All comparative claims (close-in vs. 3GPP fit, NLoS vs. LoS differences in path-loss exponent and SF, frequency trends in RMS-DS from 93.11 ns to 46.84 ns and K-factor from 9.16 dB to 12.88 dB, and the distributional characterizations) rest on this binary partition; even moderate misclassification rates would directly affect the reported state- and frequency-dependent statistics.
minor comments (2)
  1. [Abstract] The abstract states concrete numerical results (e.g., RMS-DS and K-factor means) without indicating the number of measurement locations, total samples, or confidence intervals; adding these would improve reproducibility.
  2. [Path loss analysis] Notation for path-loss models and shadow-fading standard deviations should be explicitly cross-referenced to the 3GPP reference equations being compared.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The primary concern is the absence of quantitative validation for the weakly supervised LoS/NLoS classification. We respond to this point below and indicate the planned revision.

read point-by-point responses

  1. Referee: Description of the weakly supervised LoS/NLoS method: no accuracy metric, precision/recall, agreement with ray-tracing, manual labels, or sensitivity analysis is reported. All comparative claims (close-in vs. 3GPP fit, NLoS vs. LoS differences in path-loss exponent and SF, frequency trends in RMS-DS from 93.11 ns to 46.84 ns and K-factor from 9.16 dB to 12.88 dB, and the distributional characterizations) rest on this binary partition; even moderate misclassification rates would directly affect the reported state- and frequency-dependent statistics.

    Authors: We acknowledge that the manuscript does not report accuracy metrics, precision/recall, agreement with ray-tracing, or manual labels for the LoS/NLoS classifier. This is inherent to the weakly supervised formulation, which deliberately avoids reliance on labeled ground truth by fusing geometric priors, channel features, and spatial consistency constraints. To address the concern that misclassification could affect the reported statistics, we will add a sensitivity analysis in the revised manuscript. The analysis will vary the relative weights of the three fusion components and quantify the resulting changes in path-loss exponents, shadow fading, RMS-DS, and K-factor distributions. The observed trends (decreasing RMS-DS and increasing K-factor with frequency) are consistent with established propagation physics, providing indirect corroboration, but the sensitivity study will directly test robustness. revision: yes

Circularity Check

0 steps flagged

No circularity; direct empirical reporting from measurements

full rationale

This is a measurement campaign paper. All central results (close-in vs 3GPP PL comparison, NLoS vs LoS differences in exponents/SF, RMS-DS drop from 93.11 ns to 46.84 ns, K-factor rise from 9.16 dB to 12.88 dB, lognormal/normal distributional characterizations) are extracted or fitted directly from the collected data after a preprocessing step (weakly supervised LoS/NLoS labeling). No equations reduce a claimed prediction or first-principles result back to the inputs by construction, no self-citations are load-bearing for uniqueness or ansatzes, and no renaming of known results occurs. The labeling method is an external input to the statistics rather than a self-referential loop. The paper is self-contained against external benchmarks (3GPP models) and reports empirical patterns without derivation chains that collapse to fitted parameters.

Axiom & Free-Parameter Ledger

4 free parameters · 2 axioms · 0 invented entities

Central claims rest on the accuracy of the LoS/NLoS classifier and the assumption that the single urban measurement campaign generalizes; all reported means and distribution parameters are fitted to the collected data.

free parameters (4)
  • path loss exponent (LoS/NLoS)
    Fitted per propagation state and frequency band to measured data
  • shadow fading standard deviation
    Fitted per condition from measured data
  • mean and variance of RMS-DS
    Computed and fitted from measured power delay profiles
  • mean and variance of Rician K-factor
    Computed and fitted from measured data
axioms (2)
  • domain assumption The weakly supervised LoS/NLoS identification method correctly separates propagation states
    Invoked to partition data before extracting statistics
  • domain assumption The chosen urban measurement routes and drone altitudes represent typical low-altitude A2G scenarios
    Required for the reported trends to apply beyond this campaign

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discussion (0)

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Reference graph

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