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REVIEW 4 major objections 4 minor 25 references

UAV Communications: Impact of Obstacles on Channel Characteristics

T0 review · 4 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This paper claims that inserting a building between a UAV-mounted Wi-Fi access point and a ground user cuts throughput from 51.8 to 41.5 Mbit/s and raises delay from 6.6 to 10.5 ms, and that moving the UAV to an unobstructed spot restores…

desk verdict Confounded propagation-model comparison and an impossible guard interval undermine an otherwise unremarkable ns-3 demonstration. read the letter →

arxiv 2412.17934 v5 pith:UO7B6LEG submitted 2024-12-23 cs.NI eess.SP

Kamal Shayegan This is my paper · ORCID
classification cs.NIeess.SP
keywords UnmannedAerialVehiclesObstacle-awareCommunicationUAVPositioningChannelCharacterizationAir-to-groundns-3simulationQualityofServiceLineSight
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper is trying to establish that obstacle-aware UAV positioning matters for air-to-ground channel quality, not just coverage geometry. It simulates a 5 GHz Wi-Fi link between a stationary UAV and a ground user in ns-3, first with clear line of sight and then with a building between them. The building lowers average throughput from 51.819 to 41.5377 Mbit/s and raises end-to-end delay from 6.61881 to 10.5386 ms, while packet delivery ratio stays at 99%. Moving the UAV back to a position where the building is no longer in the way restores the original throughput and delay. If true, this supports building obstacle awareness into UAV placement algorithms, since higher-frequency future links will be increasingly blockage-sensitive.

What carries the argument

The central machinery is the pair of propagation-loss models in the ns-3 simulator: the Friis free-space path-loss model for the obstacle-free scenario and the HybridBuildingsPropagationLossModel for the obstacle scenario, the latter adding building-dependent attenuation and shadowing to the same 5 GHz Wi-Fi link. The building block is a rectangular obstacle with coordinates x_min=10, x_max=20, y_min=0, y_max=50, z_min=-30, z_max=30, and the UAV moves between position 1 (30,0,10) and position 2 behind the building. These models carry the argument by producing the throughput, delay, and delivery metrics that are compared across the obstructed and unobstructed configurations.

What would settle it

Run the same scenario in the simulator with the building present but with its attenuation set to zero while keeping all other parameters; if throughput and delay do not return to the unobstructed values, the reported degradation comes from the obstacle attenuation model. Conversely, a field test with a small UAV at 10 m altitude, 30 m horizontal distance, and a building between it and a 5 GHz ground client would show whether a roughly 20% throughput drop and a 4 ms delay increase actually occur.

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Extended reading notes

Core claim

In this paper's terms, the central discovery is that a single static obstacle placed in an air-to-ground Wi-Fi link measurably degrades channel quality, and that repositioning the UAV to re-establish line of sight recovers that quality. The claim is supported by ns-3 simulations at 5 GHz: with the Friis propagation loss model and no obstacle, average throughput is 51.819 Mbit/s and end-to-end delay is 6.61881 ms; with HybridBuildingsPropagationLossModel and a building spanning x=10–20 m, y=0–50 m, z=±30 m, throughput falls to 41.5377 Mbit/s and delay rises to 10.5386 ms, while PDR remains 99% and packet loss remains 1%. The paper further reports that raising the frequency to 10 GHz under the same configuration decreases throughput further, and that relocating the access point to the original unobstructed position restores performance. The intended conclusion is that channel characterization for UAV links should include obstacles, and UAV placement should treat line-of-sight as a primary optimization criterion.

Load-bearing premise

The load-bearing premise is that the simulator's building attenuation model, as configured here, captures how a real building blocks a 5 GHz signal; if the configuration is unrealistic, the measured throughput drop may be a simulation artifact rather than a real obstacle effect.

Editorial extensions

If this is right

  • If the claim is correct, UAV placement algorithms should treat line of sight as a first-class optimization target, not just coverage radius or user density.
  • Obstacle-aware repositioning is a workable recovery mechanism: in this scenario, moving the access point from the blocked position back to the unobstructed position restores the original throughput and delay values.
  • Carrier frequency strengthens the effect: the paper's 10 GHz run shows throughput falling further below the 5 GHz obstructed case, so obstacle awareness should matter more for higher-frequency air-to-ground links.
  • Channel characterization for aerial links should routinely report both line-of-sight and non-line-of-sight configurations, because throughput and delay are strongly configuration-dependent.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Going beyond the paper, the roughly 20% throughput penalty could be used as a simple scalar objective for obstacle-aware placement: maximize the fraction of users with clear line of sight to the UAV, and the QoS metrics should follow.
  • A testable extension is to vary the building's height, width, and horizontal offset; one would predict that throughput degradation grows as the building increasingly blocks the direct path, with a threshold above which packet delivery ratio begins to fall.
  • The setup could also be extended to multiple UAVs or moving blockers; a natural prediction is that blockage recovery time, rather than steady-state throughput, becomes the dominant QoS factor when obstacles move.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper proposes a simulation-based study of an air-to-ground Wi-Fi link between a UAV-mounted access point and a ground user in ns-3. It compares an obstacle-free scenario using the Friis propagation loss model with an obstacle scenario using the HybridBuildingsPropagationLossModel, reporting average throughput, PDR, packet loss, and end-to-end delay over 10 runs. The authors claim that placing a building between the UAV and the user degrades QoS and that moving the UAV to a line-of-sight position restores performance. They position the work as an obstacle-aware channel characterization and as a step toward obstacle-aware UAV positioning.

Significance. If the claims were established, the paper would provide a modest simulation-based confirmation that 5 GHz air-to-ground links degrade when a building blocks line of sight and that repositioning recovers performance. The paper has some strengths: the simulation parameters are listed in tables, and the use of 10 repeated runs is better than a single run. However, the central comparison conflates the propagation model with the presence of the obstacle, the reported statistics lack any uncertainty measure, and a listed Wi-Fi guard interval of 100 ms is physically implausible for 802.11ac. As written, the result is largely a demonstration of ns-3's built-in building attenuation rather than an independent characterization of air-to-ground channels, so its significance for the UAV placement literature is currently limited.

major comments (4)
  1. [Section IV-A and IV-C] The obstacle-free and obstacle scenarios differ in two variables at once: the presence of the building and the propagation loss model. Scenario 1 uses the Friis path loss model, while scenario 2 uses HybridBuildingsPropagationLossModel. No control run with HybridBuildingsPropagationLossModel and an empty scene is reported. The observed drop in average throughput from 51.819 to 41.5377 Mbit/s and the increase in delay from 6.61881 to 10.5386 ms are therefore not causally attributable to the obstacle. This is load-bearing because the paper's conclusion rests on attributing the QoS change to the building. A control experiment with the same propagation model and the building present/absent, or an otherwise identical setup using one propagation model, is needed before the central claim can be evaluated.
  2. [Section IV-C and Tables I-II] The paper reports averages over 10 simulation runs but provides no standard deviations, confidence intervals, per-run values, or distribution plots for the main metrics. Without a measure of run-to-run variability, the reported differences in throughput and delay cannot be distinguished from simulation noise. The authors should report variance or confidence intervals, and ideally show per-run data or box plots, to support the quantitative comparison.
  3. [Tables I and II] The guard interval is listed as 100 ms. In 802.11ac OFDM, guard intervals are on the order of 0.4 to 3.2 microseconds, so 100 ms is not a physically valid parameter and suggests a unit or configuration error. This parameter undermines the fidelity of the reported results. The authors must correct the guard interval to a standard 802.11ac value and rerun the simulations, or explicitly justify why 100 ms was used.
  4. [Sections I, V, and VI] The central claim is substantially overstated relative to the evidence. A single static scenario with one building, one ground user, and one frequency band is used to support a broad conclusion about UAV positioning with obstacles improving QoS and coverage. Moreover, since HybridBuildingsPropagationLossModel is specifically designed to add building attenuation, the simulation outcome largely restates the model's assumptions rather than providing an independent empirical characterization. The paper should be reframed as a demonstration of an ns-3 simulation workflow, with the scope clearly limited to this scenario, or it should include additional scenarios and a validation discussion.
minor comments (4)
  1. [Figures 4-8] The figure captions contain repeated typos and are internally inconsistent: 'Throghput' appears in several captions, Fig. 8 is captioned as a probability distribution function but appears to show a throughput time series, and Section VI refers to a 'Fig. 9' that does not exist.
  2. [Tables I and II] Table II omits the packet size row that is present in Table I, so the two listed configurations are not fully comparable as presented. The table headers also misspell 'SCERARIO'.
  3. [Section I] The statement that obstacle-aware UAV repositioning 'has not been investigated so far' is contradicted by the paper's own references, including [1], [2], and [22]. The novelty claim should be softened and positioned relative to those works.
  4. [Abstract and Section IV-B] There are typographical issues such as 'Th e' in the abstract and 'Tthroughput' in the metrics list. These should be corrected for readability.

Circularity Check

1 steps flagged · score 6.0 of 10

Obstacle effect is pre-encoded: switching from Friis to HybridBuildingsPropagationLossModel makes the QoS drop a model output, not a measured discovery.

  1. self definitional [Section IV-A (Simulation Setup), Section IV-C (Simulation Results), and Section VI (Main Conclusions)]
    "We employed the Friis propagation loss model in an obstacle-free scenario and a hybrid building pr opagation loss model in case we had obstacles in the scenario. ... In the second scenario, where F = 5GHz, and we have deployed the building as an obstacle, the obtained throughput has decreased and the delay has increased. The average throughput in this case was 41.5377 Mbit/s ... and the end-to-end delay was 10.5386 ms."

    The first scenario uses Friis (free-space) loss; the second uses HybridBuildingsPropagationLossModel, an ns-3 model specifically designed to add building-dependent losses. Inserting a building into that model necessarily produces attenuation, so the observed throughput drop (51.819 to 41.5377 Mbit/s) and delay increase (6.61881 to 10.5386 ms) are direct outputs of the model's built-in rules. The two scenarios differ in both the obstacle and the propagation model, and no control run with HybridBuildingsPropagationLossModel and an empty scene is reported. The conclusion that obstacles degrade QoS and that LoS repositioning restores it is therefore a restatement of the simulator's geometric blockage logic, not an independent measurement.

full rationale

This paper is a simulation study rather than a mathematical derivation, so the usual circularity tests must be applied to the inference from simulation configuration to conclusion. The central claim—that placing a building between UAV and UE degrades throughput and delay, and that moving to LoS restores performance—is supported only by comparing a Friis-model scenario against a HybridBuildingsPropagationLossModel scenario. Because HybridBuildingsPropagationLossModel is defined to attenuate signals when buildings obstruct the path, the simulated degradation is a tautological consequence of the chosen model; the absence of a same-model, no-building control means the building's marginal effect is never isolated. The self-citations in the reference list are background/related-work citations and are not load-bearing, so they do not independently raise the score. The central result reduces to the simulator's built-in behavior, giving a circularity score of 6 rather than 0-2. A non-circular version would use the same propagation model with and without the obstacle, or validate against real channel measurements.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new entities and fits no data, but it relies on a set of hand-picked scenario parameters and on the fidelity of ns-3's propagation models. The building blockage result is largely a consequence of the chosen HybridBuildingsPropagationLossModel, which already encodes the obstruction effect.

free parameters (4)
  • UAV altitude = 10 m
    Chosen by hand as the height of the Wi-Fi AP; the quantitative results depend on it.
  • Building footprint = x 10-20, y 0-50, z -30 to 30
    Obstacle dimensions chosen by hand; they set the blockage geometry.
  • Guard interval = 100 ms
    Listed in Tables I and II; physically implausible for 802.11ac and likely a typo, but it is an input to the simulation.
  • Carrier frequency = 5 GHz and 10 GHz
    Hand-selected bands to compare; the paper claims higher frequencies are more obstructed but only presents one 10 GHz throughput plot.
assumptions (4)
  • domain assumption ns-3's Friis and HybridBuildingsPropagationLossModel accurately model 5 GHz air-to-ground propagation.
    The entire measurement rests on simulator fidelity; Section IV-A and IV-C.
  • domain assumption The building fully blocks line of sight in the NLoS scenario and no diffracted or reflected path is material.
    The paper attributes the throughput drop to LoS blockage without considering multipath alternatives; Section III and IV-C.
  • ad hoc to paper The listed simulation parameters are physically valid and consistent.
    The 100 ms guard interval in Tables I and II contradicts 802.11ac norms; the paper never addresses this.
  • domain assumption Ten simulation runs of an ns-3 scenario are sufficient to report averages without variance.
    No seeds or confidence intervals are reported; Section IV-C.

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Pith. "Pith review of UAV Communications: Impact of Obstacles on Channel Characteristics." pith.science (2026). https://pith.science/paper/UO7B6LEG

@misc{pith2026241217934,
  author       = {Pith},
  title        = {Pith review of: UAV Communications: Impact of Obstacles on Channel Characteristics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UO7B6LEG}},
  note         = {Machine review of arXiv:2412.17934}
}
read the original abstract

In recent years, Unmanned Aerial Vehicles (UAVs) have been utilized as effective platforms for carrying Wi-Fi Access Points (APs) and cellular Base Stations (BSs), enabling low-cost, agile, and flexible wireless networks with high Quality of Service (QoS). The next generation of wireless communications will rely on increasingly higher frequencies, which are easily obstructed by obstacles. One of the most critical concepts yet to be fully addressed is positioning the UAV at optimal coordinates while accounting for obstacles. To ensure a line of sight (LoS) between UAVs and user equipment (UE), improve QoS, and establish reliable wireless links with maximum coverage, obstacles must be integrated into the proposed placement algorithms. This paper introduces a simulation-based measurement approach for characterizing an air-to-ground (AG) channel in a simple scenario. By considering obstacles, we present a novel perspective on channel characterization. The results, in terms of throughput, packet delivery, packet loss, and delay, are compared using the proposed positioning approach.

Figures

Figures reproduced from arXiv: 2412.17934 by the authors.

Figure 1
Figure 1. Wireless network providing by UAV in a scenario without obstacle [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Wireless network providing by UAV in a scenario with obstacle IV. MEASUREMENTS SETUP The methodology followed for the Measurements from the simulation of both scenarios is presented in this section, including the simulation setup, the measurement metrics, and the results. A. Simulation Setup As [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Manifestations for the first scenario. A user in (0.0, 0.0, 0.0) coordinate and a UAV in (30.0, 00.0, 10.0). In the second scenario, we have deployed a building as an obstacle between the AP and the ground user. In terms of mobility models, we have used ns3::ConstantPosition￾MobilityModel for the Wi-Fi AP and the same for the user. Since the position of the obstacle in this scenario is constant, the MobilityBuilding… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Throghput variation for obstacle-free scenarion for F = 5 GHz [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Throghput variation for non-obstacle-free scenarion for F = 5GHz In this case if the frequency increased to 10 GHz, with the same configuration, the throughput is decreased. The variation of 10 iterations of throughput have shown in in [PITH_FULL_IMAGE:figures/full_fi…
Figure 8
Figure 8. Figure 8: probability distribution function of throughput after putting the obstacle in scenario for F = 5 GHz. VI. MAIN CONCLUSIONS We presented a characterization of a wireless link between a UAV and a UE in this paper, where there is an obstacle between the provider and the g…

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Reference graph

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Reviewed August 11, 2026 · model on record in the stance chip above.