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arxiv: 1907.02603 · v1 · pith:I6SKVRP7new · submitted 2019-07-04 · 📡 eess.SP

Ray Tracing Analysis for UAV-assisted Integrated Access and Backhaul Millimeter Wave Networks

Alberto Perez , Abdurrahman Fouda , Ahmed S. Ibrahim This is my paper

Pith reviewed 2026-05-25 08:39 UTC · model grok-4.3

classification 📡 eess.SP
keywords UAV relaymmWaveintegrated access and backhaulray tracingamplify-and-forwarddecode-and-forwardcoverageSINR
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The pith

Ray tracing simulations show UAV relays improve downlink coverage and SINR in mmWave IAB networks at 30 and 60 GHz.

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

The paper establishes that UAVs deployed as hovering relays can deliver measurable gains in coverage and signal quality for millimeter-wave integrated access and backhaul cellular systems. It implements amplify-and-forward and decode-and-forward relaying inside a commercial ray-tracing engine and uses the resulting propagation maps to place the UAVs in three dimensions. The simulations compare the relayed links against direct base-station coverage in a dense urban layout and report higher received SINR when the UAVs are active. A sympathetic reader would care because mmWave signals suffer severe path loss; any practical way to restore coverage without adding many fixed base stations would change network planning.

Core claim

Ray-tracing analysis demonstrates that UAV-assisted AF and DF relaying in IAB mmWave networks produces higher downlink coverage and improved SINR compared with direct transmission, with UAV locations chosen from access and backhaul coverage maps and with adaptive power control applied to the AF case.

What carries the argument

WinProp ray-tracing engine with custom AF and DF relaying implementations that generate 3D coverage maps at 30 GHz and 60 GHz to determine UAV placement and transmission parameters.

If this is right

  • UAV height and horizontal position can be selected directly from separate access-link and backhaul-link coverage maps.
  • Adaptive UAV transmit power for AF mode further improves the received SINR distribution.
  • Both AF and DF modes yield visible coverage extensions at both 30 GHz and 60 GHz.
  • The same ray-tracing workflow can be repeated for other urban geometries or frequency pairs.

Where Pith is reading between the lines

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

  • If the ray-tracing model is later validated against measurements, the same workflow could guide real-time UAV repositioning based on live coverage data.
  • The approach may apply to non-urban settings once the propagation engine is recalibrated for different building materials and foliage.
  • Network operators could use these coverage maps to decide when to activate UAV relays versus adding fixed small cells.

Load-bearing premise

The ray-tracing predictions match actual outdoor mmWave propagation behavior at 30 and 60 GHz in a Manhattan-like setting.

What would settle it

Field measurements at 30 or 60 GHz in a comparable urban environment that show coverage or SINR values differing substantially from the ray-tracing results for the same UAV positions and relaying modes.

Figures

Figures reproduced from arXiv: 1907.02603 by Abdurrahman Fouda, Ahmed S. Ibrahim, Alberto Perez.

Figure 1
Figure 1. Figure 1: UAV-assisted integrated access and backhaul. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Received Downlink SINR from single IAB-donor. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Antenna radiation pattern [PITH_FULL_IMAGE:figures/full_fig_p002_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Received SINR levels at backhaul link of a single UAV. [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 8
Figure 8. Figure 8: Received downlink SINR map at backhaul link of a [PITH_FULL_IMAGE:figures/full_fig_p004_8.png] view at source ↗
Figure 6
Figure 6. Figure 6: AF relaying mode: CDF of downlink received SINR. [PITH_FULL_IMAGE:figures/full_fig_p004_6.png] view at source ↗
Figure 10
Figure 10. Figure 10: DF relaying mode: CDF of downlink received SINR. [PITH_FULL_IMAGE:figures/full_fig_p005_10.png] view at source ↗
read the original abstract

The use of Millimeter-wave (mmWave) spectrum in cellular communications has recently attracted growing interest to support the expected massive increase in traffic demands. However, the high path-loss at mmWave frequencies poses severe challenges. In this paper, we analyze the potential coverage gains of using unmanned aerial vehicles (UAVs), as hovering relays, in integrated access and backhaul (IAB) mmWave cellular scenarios. Specifically, we utilize the WinProp software package, which employs ray tracing methodology, to study the propagation characteristics of outdoor mmWave channels at 30 and 60 GHz frequency bands in a Manhattan-like environment. In doing so, we propose the implementation of amplify-and-forward (AF) and decode-and-forward (DF) relaying mechanisms in the WinProp software. We show how the 3D deployment of UAVs can be defined based on the coverage ray tracing maps at access and backhaul links. Furthermore, we propose an adaptive UAV transmission power for the AF relaying. We demonstrate, with the aid of ray tracing simulations, the performance gains of the proposed relaying modes in terms of downlink coverage, and the received signal to interference and noise ratio (SINR).

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

2 major / 2 minor

Summary. The manuscript uses the WinProp ray-tracing package to simulate outdoor mmWave propagation at 30 and 60 GHz in a Manhattan-like environment. It proposes custom implementations of AF and DF relaying for UAVs acting as IAB relays, an adaptive UAV transmit-power rule for AF, and 3D UAV placement derived from coverage maps. The central claim is that these relaying modes yield measurable gains in downlink coverage probability and SINR relative to direct links.

Significance. If the underlying propagation predictions are reliable, the work provides a concrete simulation-based illustration of how UAV height, power adaptation, and relaying mode affect IAB mmWave coverage in a dense urban setting. The explicit mapping from ray-tracing maps to UAV locations and the AF power-control rule are useful engineering contributions. However, the absence of any measurement validation or error quantification against real 30/60 GHz urban channels substantially reduces the strength of the claimed gains.

major comments (2)
  1. [Abstract / simulation methodology] Abstract and simulation-setup description: the headline claim that AF/DF relaying produces coverage and SINR gains rests entirely on WinProp predictions, yet no calibration, measurement comparison, or error statistics versus field data at 30 or 60 GHz are supplied. This is load-bearing because the quantitative results cannot be assessed without evidence that the material models, diffraction, and scattering assumptions match real Manhattan-like channels.
  2. [Abstract] Abstract: the text states that performance gains are demonstrated but supplies neither numerical coverage probabilities, SINR CDFs, nor any comparison tables or figures with error bars. Without these data the magnitude and statistical significance of the reported improvements cannot be evaluated.
minor comments (2)
  1. The description of how AF and DF are coded inside WinProp (gain, noise figure, decoding threshold, etc.) is referenced but not given in sufficient algorithmic detail for reproducibility.
  2. Notation for the adaptive power rule and the exact definition of the coverage maps used for UAV placement should be formalized with equations rather than prose.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive comments on our simulation-based study. We address each major comment below, noting the simulation nature of the work using WinProp.

read point-by-point responses

  1. Referee: [Abstract / simulation methodology] Abstract and simulation-setup description: the headline claim that AF/DF relaying produces coverage and SINR gains rests entirely on WinProp predictions, yet no calibration, measurement comparison, or error statistics versus field data at 30 or 60 GHz are supplied. This is load-bearing because the quantitative results cannot be assessed without evidence that the material models, diffraction, and scattering assumptions match real Manhattan-like channels.

    Authors: We acknowledge that the study is entirely simulation-based and provides no new measurement validation or error statistics against real 30/60 GHz urban data. WinProp is a commercial ray-tracing tool whose propagation models (including diffraction and scattering) have been used in prior mmWave literature; we will add references to existing validation studies of WinProp in similar urban settings and expand the methodology section to discuss model assumptions and limitations. New field measurements cannot be added within the scope of this work. revision: partial

  2. Referee: [Abstract] Abstract: the text states that performance gains are demonstrated but supplies neither numerical coverage probabilities, SINR CDFs, nor any comparison tables or figures with error bars. Without these data the magnitude and statistical significance of the reported improvements cannot be evaluated.

    Authors: The manuscript body contains the full simulation results, including coverage probability plots and SINR CDF comparisons (with the proposed AF/DF modes versus direct links). To improve clarity, we will revise the abstract to include specific numerical examples of the observed gains drawn from those results. revision: yes

standing simulated objections not resolved
  • Absence of any measurement validation or error quantification against real 30/60 GHz urban channels

Circularity Check

0 steps flagged

No circularity: pure simulation study with no derivations or fitted models

full rationale

The paper performs ray-tracing simulations in WinProp to evaluate UAV relaying gains in mmWave IAB networks. No analytic derivations, equations, or parameter-fitting steps are described that could reduce to inputs by construction. Claims rest on simulation outputs rather than any self-referential prediction or self-citation chain. This matches the reader's assessment of zero circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Simulation study using commercial software; no free parameters, axioms, or invented entities are introduced or fitted in the provided abstract.

pith-pipeline@v0.9.0 · 5744 in / 1040 out tokens · 24545 ms · 2026-05-25T08:39:46.399811+00:00 · methodology

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