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REVIEW 3 major objections 5 minor 37 references

High-Fidelity RF Mapping: Assessing Environmental Modeling in 6G Network Digital Twins

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This paper proposes Hausdorff and chamfer point-cloud distances that quantify how environmental modeling changes—adding parked vehicles or segmenting building windows—alter the delay, power, and angle features of simulated radio channels…

desk verdict The metric idea is sensible and the case studies are real, but the reported dB/ns/degree numbers do not follow from the equations, so the quantitative claims need a major fix. read the letter →

arxiv 2507.19173 v1 pith:ZMTMM24L submitted 2025-07-25 eess.SP cs.NI

Lorenzo Cazzella , Francesco Linsalata , Damiano Badini , Matteo Matteucci , Maurizio Magarini , Umberto Spagnolini This is my paper · ORCID
classification eess.SPcs.NI
keywords DigitalTwinRaytracingEnvironmentalmodelingHausdorffdistanceChamferPointcloudcomparison28GHzpropagationVehicularsimulation
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

This paper is trying to establish a quantitative, localizable way to ask when a digital twin of an electromagnetic environment is good enough: given two ray tracing simulations for the same transmitter and receiver over 3D scenes whose modeling details differ, the proposed Hausdorff and chamfer ray-tracing distances (HRT and CRT) convert the difference into numbers in the delay, power, and angular domains. The motivation is practical, because 6G network digital twins depend on environmental fidelity and builders need to know which mesh details actually change propagation before investing in them. Tested on a 550 by 670 meter digital twin of a Milan urban area at 28 GHz, with parked vehicles added and building facades split into glass window components, both metrics flag the same geographic regions as most affected, concentrated near the base station, and the vehicular simulations trace how those differences evolve along realistic routes. If the approach is right, it gives network designers an interpretable tool for deciding where modeling effort matters and for checking that a simplified environment still reproduces the radio behavior of a detailed one.

What carries the argument

The central object is the ray-path point cloud: each simulated path is a 6-tuple $(P,\tau,\theta_{\mathrm{DoD}},\varphi_{\mathrm{DoD}},\theta_{\mathrm{DoA}},\varphi_{\mathrm{DoA}})$ in $\mathbb{R}^6$, with power and delay standardized to zero mean and unit variance within each scenario and angular differences measured by the cosine distance $1-\hat{u}(\theta,\varphi)\cdot\hat{u}(\theta',\varphi')$ between free-space unit vectors. On these clouds the aggregate distance $d_{\mathrm{R}}=d_\tau+d_P+d_{\mathrm{DoD}}+d_{\mathrm{DoA}}$ defines the nearest-neighbor assignment, and two bidirectional set distances summarize the result: the Hausdorff distance, a worst-case (maximum) statistic, and the Chamfer distance, an average statistic, each symmetrized over the two sets. This machinery does the work of comparing simulations that produce different numbers of paths, since it never requires a one-to-one correspondence between rays.

What would settle it

Take two simulations of the same scene and add a constant power offset, say +6 dB, to every path in one of them; under the stated per-scenario standardization both HRT and CRT report a power distance of zero, which would demonstrate that the metric measures only relative shape and cannot detect absolute level changes. Conversely, recomputing the reported 16 dB average power distance directly from equations (3) and (7) would require the missing transformation from standardized units to decibels, and its absence would settle whether the reported numbers are well-defined.

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Extended reading notes

Core claim

The paper's central claim is that a propagation path can be represented as a point in a six-dimensional space—received power, delay, departure azimuth and elevation, and arrival azimuth and elevation—and that the bidirectional Hausdorff and Chamfer distances between two such point clouds form a consistent and interpretable measure of how an environmental change alters the simulated channel. Distances in power and delay are computed on per-scenario standardized values, the angular parts are cosine distances between unit vectors, and the four components are summed into one aggregate distance that drives the nearest-neighbor search while each feature's contribution is separately recorded. Applied to the Milan digital twin, the metrics show that adding 505 parked vehicles changes simulated propagation mostly near the base station, with average HRT power differences around 16 dB on the grid and up to 35 dB along a vehicular trajectory, while switching building windows from concrete to glass changes only the power feature, up to about 2.4 dB in HRT and 0.75 dB in CRT. The fact that HRT and CRT flag the same areas, with the maximum-type statistic consistently above the average-type one, is what the authors take as evidence that the comparison is trustworthy.

Load-bearing premise

The load-bearing premise is that the power and delay distances, which are computed on values standardized separately for each scenario, can be reported as physical differences in decibels and nanoseconds as done in the results section, even though the paper never states the mapping between the standardized distances and those physical units, and the per-scenario standardization makes any uniform offset between the two scenarios invisible to the metrics.

Editorial extensions

If this is right

  • Because the distances operate on sets of unequal cardinality through nearest-neighbor matching under the joint metric, any two simulation runs can be compared without one-to-one path correspondence.
  • The per-feature tracking means one computation yields separate delay, power, departure-angle, and arrival-angle distances, so an engineer can see which physical feature a modeling change actually perturbs.
  • In the two Milan case studies, both metrics locate the main differences near the base station, suggesting that for dense urban deployments the fidelity of close-range modeling dominates the simulated channel.
  • The vehicular runs show how HRT and CRT evolve along a trajectory, identifying the stretches of a route where the environmental model has the largest effect on the simulated link.
  • The authors report analogous patterns at 7 GHz, indicating the comparison procedure is not specific to the 28 GHz band.

Reading between the lines

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

  • Because each scenario's power and delay are standardized by their own mean and standard deviation, a uniform offset between the two scenarios—say a global shadowing event or a wrong absorption constant for a material—produces zero distance, so the metric is a relative rather than an absolute fidelity check.
  • The concentration of differences near the base station may be partly an artifact of the shooting-and-bouncing-ray sampling, which launches a fixed number of candidate rays and therefore rarely reaches distant vehicles, so the claimed geography of modeling impact should be re-checked with an exhaustive ray tracer before being treated as physical.
  • A natural extension the paper does not develop is to use HRT and CRT as a loss function for calibrating material parameters: adjust the simulated materials until the point-cloud distance to a measured set of paths is minimized.
  • The windows case, where only the power feature moves and reachable geometry is identical, suggests the metrics can be used to separate material-calibration errors from geometric modeling errors in mixed scenes.
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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

3 major / 5 minor

Summary. The paper proposes two dissimilarity measures, the Hausdorff ray tracing (HRT) and Chamfer ray tracing (CRT) distances, to compare the multipath parameters (power, delay, departure and arrival angles) produced by ray tracing simulations of the same transmitter-receiver pair under different environmental-modeling assumptions. The method represents each set of propagation paths as a point cloud in a six-dimensional space, standardizes the power and delay dimensions using per-set means and standard deviations, and computes a composite tuple distance dR (Eq. 4) that combines standardized power and delay absolute differences with cosine distances for DoD and DoA. HRT and CRT are then defined as bidirectional Hausdorff and Chamfer statistics on these distances. The authors evaluate the metrics on a digital twin of an area of Milan, Italy, comparing a baseline scenario with (i) a scenario enriched with 505 parked vehicle meshes and (ii) a scenario with segmented building windows assigned a different radio material, using Sionna RT and SUMO at 28 GHz. The results are presented as maps and trajectory plots reporting the distances in dB, nanoseconds, and degrees.

Significance. If the proposed metrics were correctly defined and the reported numbers were derivable from them, the approach would be a useful tool for assessing the fidelity of environmental models in digital twins: it offers a quantitative and spatially localizable comparison of how changes in 3D scene geometry and materials alter simulated radio channels, and it handles sets of paths with different cardinality. The experimental setup is substantial: a high-fidelity urban model, realistic vehicle placement, manual facade segmentation, and integration of two open-source simulators. The paper also contributes two concrete case studies with a reproducible simulation pipeline using publicly documented tools (Sionna RT, SUMO, Blender), although no code or dataset is released. However, the central quantitative claims are undermined by inconsistencies between the formal definitions and the reported physical units, so the current version does not support the stated objective of 'consistently comparing temporal, angular and power features' in physical terms.

major comments (3)
  1. [Section 3.2 (Eq. 3) and Section 4.2]
  2. [Section 3.2 (standardization) and Section 4.3]
  3. [Section 3.3 and Section 4.2]
minor comments (5)
  1. [Section 4.2, paragraph on grid-based simulations]
  2. [Table 1]
  3. [Section 3.2 and Section 4.4]
  4. [Section 4.2]
  5. [References]

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the HRT/CRT metrics are self-contained definitions and no prediction reduces to its inputs.

full rationale

The paper proposes HRT and CRT as applications of the standard Hausdorff and Chamfer distances to ray-tracing parameter sets. Eq. (3) defines per-feature distances from the standardized delay/power values and angular cosine distances, and Eqs. (4)-(7) combine them without fitted parameters or reference to any target result. The central comparison—base scenario versus vehicle-enriched or window-segmented scenarios—is an external simulation study, not a quantity derived from the metric definitions themselves. The agreement between HRT and CRT is presented descriptively ('similar patterns arise on the grid for both the HRT and the CRT'), and both are just max and mean statistics of the same underlying nearest-neighbor distances, so the paper does not claim an independent confirmation there; moreover, this is not a derivation of one result from another. Self-citations in the introduction and related work (e.g., [3], [5], [7], [12]) concern digital-twin frameworks and ray-launching benchmarks, not the proposed distance formulas, so they are not load-bearing for the metric construction. The main weakness is a units/interpretation gap: dP and dtau in Eq. (3) are dimensionless after per-set standardization, while Section 4 reports 'Power distance (dB)', 'Delay distance (nsec)', and angular distances in degrees without stating a conversion. That is a correctness or presentation issue, not a circularity, because the reported numbers are not asserted to follow from the equations alone. No load-bearing step reduces to an input by construction, so the circularity score is 0.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

No new physical entities are introduced. The central method depends on three free design choices: standardization moments, component weights, and the distance aggregation. It also relies on four domain assumptions about simulator correctness, material parameters, and the meaningfulness of point-cloud distances for channel comparison.

free parameters (2)
  • Per-set standardization moments (mu_P, sigma_P, mu_tau, sigma_tau) = Computed from each scenario's ray database; not numerically specified
    The composite distance dR depends on these values; standardizing each set separately removes global power and delay offsets between scenarios, so a uniform shift in power would be invisible to the metric.
  • Component weights in Eq. (4) = Uniform 1.0
    Chosen by hand; no sensitivity analysis or justification for equal weighting of delay, power, DoD, and DoA.
assumptions (4)
  • domain assumption Hausdorff and Chamfer distances on ray parameter point clouds are meaningful summary statistics for channel differences
    No theoretical justification is given for why these point-cloud distances capture practically relevant channel changes; their usefulness is assumed through the case study.
  • domain assumption Sionna RT with Fibonacci ray launching provides sufficiently accurate and complete ray parameter sets
    The method relies on the simulator's correctness; the paper notes Fibonacci 'provides no guarantees that every possible path is found' (Section 4.1), which could bias the sets being compared.
  • ad hoc to paper Per-set standardization yields comparable dimensionless features across scenarios
    Standard practice would use a common reference distribution; here each set is standardized with its own mean and std, so absolute power and delay levels are not compared.
  • domain assumption ITU P.2040 material parameters at 28 GHz are accurate for concrete, glass, and PEC in this scenario
    Radio material properties are taken from ITU recommendation without measurement-based calibration.

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Cite this review

Pith. "Pith review of High-Fidelity RF Mapping: Assessing Environmental Modeling in 6G Network Digital Twins." pith.science (2026). https://pith.science/paper/ZMTMM24L

@misc{pith2026250719173,
  author       = {Pith},
  title        = {Pith review of: High-Fidelity RF Mapping: Assessing Environmental Modeling in 6G Network Digital Twins},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZMTMM24L}},
  note         = {Machine review of arXiv:2507.19173}
}
read the original abstract

The design of accurate Digital Twins (DTs) of electromagnetic environments strictly depends on the fidelity of the underlying environmental modeling. Evaluating the differences among diverse levels of modeling accuracy is key to determine the relevance of the model features towards both efficient and accurate DT simulations. In this paper, we propose two metrics, the Hausdorff ray tracing (HRT) and chamfer ray tracing (CRT) distances, to consistently compare the temporal, angular and power features between two ray tracing simulations performed on 3D scenarios featured by environmental changes. To evaluate the introduced metrics, we considered a high-fidelity digital twin model of an area of Milan, Italy and we enriched it with two different types of environmental changes: (i) the inclusion of parked vehicles meshes, and (ii) the segmentation of the buildings facade faces to separate the windows mesh components from the rest of the building. We performed grid-based and vehicular ray tracing simulations at 28 GHz carrier frequency on the obtained scenarios integrating the NVIDIA Sionna RT ray tracing simulator with the SUMO vehicular traffic simulator. Both the HRT and CRT metrics highlighted the areas of the scenarios where the simulated radio propagation features differ owing to the introduced mesh integrations, while the vehicular ray tracing simulations allowed to uncover the distance patterns arising along realistic vehicular trajectories.

Figures

Figures reproduced from arXiv: 2507.19173 by the authors.

Figure 1
Figure 1. Considered DT scenario of an area of Milan, Italy. (a) presents a top view of [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Considered vehicle meshes for parked vehicles modeling. The depicted models [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Base scenario enriched with parked vehicles. (a) shows a front view of the [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Windows segmentation pipeline. In the following, we discuss in detail the followed pipeline, which we provide in [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Base scenario enriched with building windows segmentation. (a) shows the [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: Simulation workflow. counting for power, temporal and angular features in the simulated rays. First, we detail the simulation setup used to produce the ray tracing simu￾lations. Then, we consider two types of environmental changes in the base propagation scenario descr…
Figure 7
Figure 7. Figure 7: Evaluated Hausdorff distance (HRT) over the delay, power and angular features [PITH_FULL_IMAGE:figures/full_fig_p018_7.png]
Figure 8
Figure 8. Figure 8: Evaluated Chamfer distance (CRT) over the delay, power and angular features [PITH_FULL_IMAGE:figures/full_fig_p019_8.png]
Figure 9
Figure 9. Figure 9: Evaluated Hausdorff (HRT) and Chamfer (CRT) distances over power for a [PITH_FULL_IMAGE:figures/full_fig_p021_9.png]
Figure 10
Figure 10. Figure 10: Power profiles of the rays produced by ray tracing simulations using Sionna RT [PITH_FULL_IMAGE:figures/full_fig_p022_10.png]
Figure 11
Figure 11. Figure 11: Evaluated Hausdorff (HRT) and Chamfer (CRT) distances over power for the [PITH_FULL_IMAGE:figures/full_fig_p023_11.png]
Figure 12
Figure 12. Figure 12: Evaluated Hausdorff (HRT) and Chamfer (CRT) distances over power for a [PITH_FULL_IMAGE:figures/full_fig_p024_12.png]

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Works this paper leans on

37 extracted references · 27 canonical work pages

  1. [1]

    Ferrand, M

    P. Ferrand, M. Amara, S. Valentin, M. Guillaud, Trends and Challenges in Wireless Channel Modeling for Evolving Radio Access, IEEE Communications Magazine 54 (7) (2016) 93–99. doi:10.1109/MCOM.2016.7509384

    arXiv 2016

  2. [2]

    L. U. Khan, W. Saad, D. Niyato, Z. Han, C. S. Hong, Digital- Twin-Enabled 6G: Vision, Architectural Trends, and Future Di- rections, IEEE Communications Magazine 60 (1) (2022) 74–80. doi:10.1109/MCOM.001.21143

    doi:10.1109/mcom.001.21143 2022

  3. [3]

    Pegurri, F

    R. Pegurri, F. Linsalata, E. Moro, J. Hoydis, U. Spagnolini, Toward digital network twins: Integrating sionna RT in ns-3 for 6G Multi-RAT networks simulations, in: IEEE INFOCOM WKSHPS: Digital Twins over NextG Wireless Networks (DTWIN 2025) (INFOCOM DTWIN 2025), London, United Kingdom (Great Britain), 2025, p. 5.88

    work page 2025

  4. [4]

    Polese, L

    M. Polese, L. Bonati, S. D’oro, S. Basagni, T. Melodia, Understand- ing O-RAN: Architecture, interfaces, algorithms, security, and research challenges, IEEE Communications Surveys & Tutorials (2023)

    work page 2023

  5. [5]

    E. Moro, F. Linsalata, M. Magarini, U. Spagnolini, A. Capone, Ad- vancing O-RAN to Facilitate Intelligence in V2X, IEEE Network (2025) 1–1doi:10.1109/MNET.2025.3553581. 27

    work page arXiv 2025

  6. [6]

    J. Gu, B. Salehi, D. Roy, K. R. Chowdhury, Multimodality in mmWave MIMO Beam Selection Using Deep Learning: Datasets and Challenges, IEEE Communications Magazine (2022)

    work page 2022

  7. [7]

    M. Zhu, L. Cazzella, F. Linsalata, M. Magarini, M. Matteucci, U. Spag- nolini, Toward Real-Time Digital Twins of EM Environments: Compu- tational Benchmark for Ray Launching Software, IEEE Open Journal of the Communications Society (2024)

    work page 2024

  8. [8]

    Lecci, P

    M. Lecci, P. Testolina, M. Polese, M. Giordani, M. Zorzi, Accuracy Versus Complexity for mmWave Ray-Tracing: A Full Stack Perspective, IEEE Transactions on Wireless Communications 20 (12) (2021) 7826–

    work page 2021

Show all 37 references
  1. [9]

    Z. Yun, M. F. Iskander, Ray Tracing for Radio Propagation Model- ing: Principles and Applications, IEEE Access 3 (2015) 1089–1100. doi:10.1109/ACCESS.2015.2453991

    2015

  2. [10]

    Alkhateeb, S

    A. Alkhateeb, S. Jiang, G. Charan, Real-Time Digital Twins: Vision and Research Directions for 6G and Beyond, IEEE Communications Magazine 61 (11) (2023) 128–134. doi:10.1109/MCOM.001.2200866

    2023 doi

  3. [11]

    Hoydis, S

    J. Hoydis, S. Cammerer, F. Ait Aoudia, A. Vem, N. Binder, G. Mar- cus, A. Keller, Sionna: An Open-Source Library for Next-Generation Physical Layer Research, arXiv preprint (Mar. 2022)

    2022

  4. [12]

    Cazzella, F

    L. Cazzella, F. Linsalata, M. Magarini, M. Matteucci, U. Spagno- lini, A Multi-Modal Simulation Framework to Enable Digital Twin- based V2X Communications in Dynamic Environments, in: 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), 2024, pp. 1–6. doi:10.1109/VT...

    2024

  5. [13]

    Bilibashi, E

    D. Bilibashi, E. M. Vitucci, V. Degli-Esposti, On Dynamic Ray Tracing and Anticipative Channel Prediction for Dynamic Environments, IEEE Transactions on Antennas and Propagation 71 (6) (2023) 5335–5348. doi:10.1109/TAP.2023.3262155

    2023

  6. [14]

    J. S. Lu, E. M. Vitucci, V. Degli-Esposti, F. Fuschini, M. Barbiroli, J. A. Blaha, H. L. Bertoni, A Discrete Environment-Driven GPU-Based Ray Launching Algorithm, IEEE Transactions on Antennas and Propagation 67 (2) (2019) 1180–1192. doi:10.1109/TAP.2018.2880036. 28

    2019

  7. [15]

    Z. Yun, M. F. Iskander, Simplifying Building Structures for Effi- cient Radio Propagation Modeling, in: 2023 IEEE International Sym- posium on Antennas and Propagation and USNC-URSI Radio Sci- ence Meeting (USNC-URSI), 2023, pp. 449–450. doi:10.1109/USNC- URSI52151.2023.10238157

    2023

  8. [16]

    Testolina, M

    P. Testolina, M. Polese, P. Johari, T. Melodia, BostonTwin: the Boston Digital Twin for Ray-Tracing in 6G Networks, in: ACM Multimedia Systems Conference 2024 (MMSys ’24), 2024. doi:https://doi.org/10.1145/3625468.3652190

    2024

  9. [17]

    Hoydis, F

    J. Hoydis, F. A. Aoudia, S. Cammerer, F. Euchner, M. Nimier- David, S. T. Brink, A. Keller, Learning Radio Environments by Differentiable Ray Tracing, IEEE Transactions on Machine Learning in Communications and Networking 2 (2024) 1527–1539. doi:10.1109/TMLCN.2024.3474639

    2024

  10. [18]

    Kanhere, T

    O. Kanhere, T. S. Rappaport, Calibration of NYURay, a 3D mmWave and sub-THz ray tracer using indoor, outdoor, and factory channel mea- surements, in: ICC 2023-IEEE International Conference on Communi- cations, IEEE, 2023, pp. 5054–5059

    2023

  11. [19]

    D. He, B. Ai, K. Guan, Z. Zhong, B. Hui, J. Kim, H. Chung, I. Kim, Channel measurement, simulation, and analysis for high-speed railway communications in 5G millimeter-wave band, IEEE Transactions on In- telligent Transportation Systems 19 (10) (2017) 3144–3158

    2017

  12. [20]

    Charbonnier, C

    R. Charbonnier, C. Lai, T. Tenoux, D. Caudill, G. Gougeon, J. Senic, C. Gentile, Y. Corre, J. Chuang, N. Golmie, Calibration of ray-tracing with diffuse scattering against 28-GHz directional urban channel mea- surements, IEEE Transactions on Vehicular Technology 69 (12) (2020)...

    2020

  13. [21]

    M. Zhu, F. Linsalata, S. Mura, L. Cazzella, D. Badini, U. Spagnolini, Exploiting Age of Information in Network Digital Twins for AI-driven Real-Time Link Blockage Detection, arXiv preprint arXiv:2505.15519 (2025)

    2025 arXiv

  14. [22]

    Bakirtzis, J

    S. Bakirtzis, J. Chen, K. Qiu, J. Zhang, I. Wassell, EM DeepRay: An expedient, generalizable, and realistic data-driven indoor propagation 29 model, IEEE Transactions on Antennas and Propagation 70 (6) (2022) 4140–4154

    2022

  15. [23]

    Zhang, H

    L. Zhang, H. Sun, J. Sun, R. Q. Hu, WiSegRT: Dataset for Site-Specific Indoor Radio Propagation Modeling with 3D Segmentation and Differ- entiable Ray-Tracing: (Invited Paper), in: 2024 International Confer- ence on Computing, Networking and Communications (ICNC), 2024, pp. 74...

    2024

  16. [24]

    Kamari, Y

    A. Kamari, Y. Chae, P. Pathak, mmSV: mmWave Vehicular Networking using Street View Imagery in Urban Environments, in: Proceedings of the 29th Annual International Conference on Mobile Computing and Networking, 2023, pp. 1–16

    2023

  17. [25]

    Levie, C ¸

    R. Levie, C ¸ . Yapar, G. Kutyniok, G. Caire, RadioUNet: Fast radio map estimation with convolutional neural networks, IEEE Transactions on Wireless Communications 20 (6) (2021) 4001–4015

    2021

  18. [26]

    Kocevska, T

    T. Kocevska, T. Javornik, A. ˇSvigelj, A. Rashkovska, A. Hrovat, Iden- tification of indoor radio environment properties from channel impulse response with machine learning models, Electronics 12 (12) (2023) 2746

    2023

  19. [27]

    OpenStreetMap, https://www.openstreetmap.org/

  20. [28]

    Dosovitskiy, G

    A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, V. Koltun, CARLA: An open urban driving simulator, in: Proceedings of the 1st Annual Conference on Robot Learning, 2017, pp. 1–16

    2017

  21. [29]

    Hoydis, S

    J. Hoydis, S. Cammerer, F. A. Aoudia, A. Vem, N. Binder, G. Marcus, A. Keller, Sionna: An open-source library for next-generation physical layer research, arXiv preprint arXiv:2203.11854 (2022)

    2022 arXiv

  22. [30]

    International Telecommunications Union Radiocommunica- tion Sector, P.2040 : Effects of Building Materials and Struc- tures on Radiowave Propagation Above About 100 MHz, https://www.itu.int/rec/R-REC-P.2040/en

  23. [31]

    Google Maps, https://www.google.it/maps, accessed on 20 March 2024. 30

    2024

  24. [32]

    P. A. Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y.-P. Fl¨ otter¨ od, R. Hilbrich, L. L¨ ucken, J. Rummel, P. Wagner, E. Wießner, Microscopic Traffic Simulation using SUMO, in: The 21st IEEE International Con- ference on Intelligent Transportation Systems, IEEE, 2018

    2018

  25. [33]

    Hoydis, F

    J. Hoydis, F. A. Aoudia, S. Cammerer, M. Nimier-David, N. Binder, G. Marcus, A. Keller, Sionna RT: Differentiable ray tracing for radio propagation modeling, arXiv preprint arXiv:2303.11103 (2023)

    2023 arXiv

  26. [34]

    Mitsuba 3 DR System, https://www.mitsuba-renderer.org/, ac- cessed on 20 March 2024

    2024

  27. [35]

    TensorFlow, https://www.tensorflow.org, accessed on 20 March 2024

    2024

  28. [36]

    Blender - 3D modeling software, https://www.blender.org/, accessed on 20 March 2024. 31

    2024

  29. [7841]

    doi:10.1109/TWC.2021.3088349

    2021

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