Multi-Modal Intelligent Channel Modeling Framework for 6G-Enabled Networked Intelligent Systems

Lu Bai; Xiang Cheng; Xuesong Cai; Zengrui Han

REVIEW 4 major objections 5 minor 15 references

Multi-Modal Intelligent Channel Modeling Framework for 6G-Enabled Networked Intelligent Systems

T0 review · 4 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read This article proposes that a 6G agent's onboard sensors—RGB cameras, depth maps, LiDAR, and mmWave radar—can serve as the input to a learned mapping that predicts radio channel characteristics, including path loss and multipath structure, i

desk verdict A clear, well-organized framework paper that mostly re-describes the authors' own prior MMICM paradigm; the empirical claims in Figs. 4-5 are unsupported and the 'unprecedented' language overreaches. read the letter →

arxiv 2509.07422 v1 pith:UL2WFBQA submitted 2025-09-09 eess.SP

Lu Bai , Zengrui Han , Xuesong Cai , Xiang Cheng This is my paper · ORCID

classification eess.SP

keywords 6G-enablednetworkedintelligentsystemsmulti-modalchannelmodelingsensingsynesthesiaofmachinespathlosspredictionscatterergenerationsensing-communicationintegration

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 article proposes MMICM, a data-driven framework that treats the radio channel as a learnable function of an intelligent agent's onboard sensors—RGB cameras, depth maps, LiDAR, and mmWave radar. It aims to predict both large-scale channel characteristics (path loss) and small-scale characteristics (multipath components, angles, Doppler) directly from synchronized sensing data, rather than from statistical distributions or costly ray tracing. The motivation is that 6G networked intelligent systems need real-time, site-specific, environment-aware channel models for tasks such as beam prediction, handover, positioning, and UAV/AGV path planning. The paper argues the framework achieves capabilities existing models lack: simultaneous prediction of hundreds of point-to-point path loss values at few-meter resolution and scene-consistent scatterer reconstruction. If the mapping holds, communication and sensing become mutually reinforcing rather than separate functions.

What carries the argument

The central object is the mapping relationship between the physical environment and the electromagnetic channel, realized through Synesthesia of Machines. The carrying mechanism is the three-module MMICM architecture—input, output, and network—anchored on synchronized multi-modal sensing-communication datasets, with modality choices driven by whether texture, geometry, or dynamics dominates the channel quantity of interest.

What would settle it

Take a model trained on synchronized sensing-communication data from one city and frequency band, deploy it at a different intersection with a different building layout and weather, and compare its predicted few-meter-resolution path-loss image pixel by pixel against measured drive-test data. If the error is no better than the 3GPP UMa/UMi statistical baseline, or if the LiDAR-derived scatterer positions do not align with visible objects, the claimed environment-to-channel mapping does not hold.

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Extended reading notes

Core claim

At the core is the claim that electromagnetic propagation can be modeled as a nonlinear mapping from multi-modal perception to channel characteristics, explored under the Synesthesia of Machines paradigm. MMICM has three modules: an output module selecting large-scale (path loss exponents, path-loss images) and small-scale (multipath components, Doppler, angles) quantities; an input module choosing and preprocessing the appropriate sensor modality (RGB for texture and material, depth/LiDAR for geometry, radar for dynamics); and a network module fusing modalities through early, mid, or late fusion with architectures suited to images or point clouds. Two illustrative examples are given: an aer

Load-bearing premise

The load-bearing premise is that a learnable, generalizable mapping exists from synchronized multi-modal sensor data (RGB, depth, LiDAR, radar) to both large- and small-scale channel characteristics, and that it transfers to scenarios, mobility regimes, and frequency bands not present in training.

Editorial extensions

If this is right

If MMICM works as described, an agent's existing sensors become a channel sounder: path-loss maps at few-meter resolution can be produced in real time without ray tracing or dense measurements, enabling closed-loop optimization of communication links and UAV/AGV trajectories.
Scene-consistent scatterer generation from LiDAR would give small-scale channel models a spatial grounding that stochastic geometry-based models lack, improving non-stationarity and temporal consistency in high-mobility scenarios.
The predicted cluster and multipath information can seed compressive channel estimation, reducing the iterations needed for CSI recovery, and can support beam prediction, cell handover, and multi-hop relay selection.
The same mapping can support cognition applications—3D reconstruction, terminal positioning, AGV navigation, and UAV path planning—effectively turning the communication channel into an additional sensing modality.
Because the framework is data-driven, its predictions can be regenerated as synthetic training data, contributing the high-fidelity multi-modal datasets that AI-native 6G systems depend on.

Reading between the lines

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

If the mapping generalizes across frequency and weather, channel prediction collapses into perception: agents could infer propagation maps for locations they can see but have never measured, inverting the current dependence on pilots and CSI feedback and freeing spectrum and energy for data rather than training overhead.
A natural testable next step, not in the paper, is to train the model on the synthetic dataset cited in the paper and evaluate on real-world measurements from a different city; that would separate genuine generalization from memorization of specific scene layouts.
The path-loss image output is naturally compatible with radio-map-based positioning and localization, suggesting MMICM could be shared across communication optimization and positioning tasks rather than modeled separately.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague, here's the quick read. This paper is an architecture/vision paper for what it calls MMICM — using multi-modal sensor data (RGB, LiDAR, radar) to predict large- and small-scale channel parameters. The idea is plausible, the writing is clear, and the survey of existing channel modeling methods is competent. The paper's best contribution is the detailed specification of the input/output design space: which sensor modalities for which channel characteristics, fusion strategies, and network architectures. That will be useful to anyone starting work in this area.

The problem is the evidence. The two figures that supposedly validate the framework (pathloss distribution and scatterer reconstruction) are qualitative. No dataset description, no model architecture, no training procedure, no hyperparameters, no quantitative error metrics. We are told MMICM 'closely matches' ground truth and beats 3GPP, but we have to take the authors' word for it. That is not enough for a claim of 'unprecedented capability'. The paper's own Section V concedes that current outputs are 'partial estimations' and that generalization 'remains limited by data-driven constraints' — which sits awkwardly with the earlier claim of cross-frequency/scenario extension capabilities.

The novelty also needs scrutiny. The core paradigm, Synesthesia of Machines, and even the MMICM name come from the authors' own prior papers ([6], [7], [8]). So this paper is best read as an exposition and extension of their paradigm, not as a new result. That is fine if presented that way, but the abstract and introduction call it 'novel' without mentioning that the paradigm has already appeared.

The citation pattern is self-heavy but not damning in itself. The issue is that [6] is cited as already establishing MMICM, which makes the marginal contribution of this paper mostly architectural detail and application speculation.

All that said, this is not a waste of time. The framework is coherent, the thinking is honest in the limitations section, and the problem is real. For a venue that accepts position papers, it deserves peer review — but the referees should demand either (a) a clear statement that the figures are illustrative, or (b) the actual experimental setup. I would not cite it in my own work until the underlying data and method are available.

Recommendation: send it to peer review with a strong request for revisions and tell the authors to tone down the 'unprecedented' claim.

Referee Report

4 major / 5 minor

Summary. This paper proposes the Multi-Modal Intelligent Channel Modeling (MMICM) framework, a data-driven paradigm that maps multi-modal sensing data (RGB, depth, LiDAR, mmWave radar) to wireless channel characteristics (large-scale and small-scale) in 6G networked intelligent systems. It reviews conventional deterministic/stochastic and AI-based channel models, describes a multi-modal sensing-communication dataset requirement, details an input/output/network-module architecture with fusion and network-architecture choices, and lists communication-augmenting and cognition-enhancing applications. Two qualitative examples (U2G pathloss distribution and V2V scatterer generation) are presented as validations against 3GPP baselines. The paper contains no equations, no training procedure, no hyperparameters, and no quantitative metrics.

Significance. The potential significance is real: if a multi-modal sensing-to-channel mapping could predict site-specific pathloss images and scatterer geometry in real time, it would enable novel closed-loop applications for UAV/AGV systems and beyond 5G/6G. The paper's taxonomy of channel modeling methods and its application catalog are useful for readers entering the area. However, the framework's empirical claims are not verifiable from the manuscript: Figs. 4-5 provide no data source, model details, or error metrics, and Sec. V itself limits the current outputs. Moreover, the core concepts (SoM, MMICM, SynthSoM) are already introduced in the authors' refs. [6]-[8], so the incremental novelty is unclear. Credit is due for a clearly organized description of a plausible pipeline and for identifying concrete open challenges (data acquisition, generalization, hybrid architectures).

major comments (4)

[Sec. III-B, Figs. 4-5] The paper's central empirical claim is unsupported. The text states that MMICM 'closely matches the ground truth' in U2G pathloss and 'accurately reconstructs the 3D positions of scatterers with better spatial consistency and density' than 3GPP TR 38.901. However, no dataset description, ground-truth source, network architecture, training procedure, hyperparameters, or error metrics are provided. Fig. 4 shows overlapping PDFs without a quantitative distance (e.g., RMSE or KS statistic), and Fig. 5 shows point clouds without detection/recall metrics. There is no held-out scene or cross-scenario experiment. These figures cannot verify the 'unprecedented capability' claim in Sec. III-B1. Either provide the experimental details and quantitative comparisons, or explicitly label the examples as illustrative and remove the superiority claims.
[Sec. V and Sec. II-C] The extension/generalization claims are asserted rather than demonstrated. Sec. II-C claims 'precise prediction capability, extension capabilities at diverse scenarios and frequency bands, and system participation capability,' but Sec. V concedes that 'current MMICM outputs are limited to partial estimations and statistical descriptions' and 'generalization remains limited by data-driven constraints.' These statements are in tension. The paper should state precisely which capabilities have been validated and which are research targets, and should temper the abstract/conclusion accordingly.
[Abstract and Refs. [6]-[8]] The novelty claim needs clarification. Reference [6] (Bai et al., IEEE COMST 2025) already introduces MMICM as 'a new modeling paradigm'; [7] introduces SoM; [8] introduces the SynthSoM dataset, all by the same group. The abstract calls the framework 'novel' without stating what this paper adds. Please identify the incremental contribution (e.g., a condensed architecture description and application survey) and avoid presenting the framework itself as new here.
[Sec. III-B1] The 'hundreds of point-to-point path loss values... unprecedented capability' sentence is the strongest quantitative claim in the paper, but the pathloss-image output is neither formalized nor evaluated. No definition of the pixel-to-pathloss mapping, resolution, loss function, or uncertainty is given. Without this, the claim is not testable. A formal problem statement with input/output spaces and evaluation protocol would be needed.

minor comments (5)

[Sec. III-A] Typo: 'wilreless communications channel data' should be 'wireless communications channel data'. Also ensure consistent hyphenation of 'spatio-temporal'.
[Sec. IV-B] Typo: 'pathloss predition' should be 'pathloss prediction'. Also, the sentence in Sec. III-B1 beginning 'For small-scale channel information' is a grammatical fragment; merge with the preceding sentence.
[Throughout] Inconsistent spacing: 'UA Vs', 'UA V', 'UAV' are used interchangeably. Please standardize (e.g., 'UAV' or 'UA V' according to journal style).
[Fig. 3 and Sec. III-B] The 'red texts mark 5 key steps' are difficult to see in the figure as rendered; please ensure adequate resolution and contrast in the final version.
[References] Refs. [6] and [8] are listed as in-press/arXiv items. If the manuscript is intended for archival publication, provide updated publication details or clearly mark them as companion papers.

Circularity Check

1 steps flagged · score 4.0 of 10

Core MMICM/SoM paradigm is a self-cited prior framework; no equation-level circularity, but the central novelty claim rests on the authors' own prior publications.

self citation load bearing [Abstract and Section II-C; references [6], [7]]
"This article presents a novel multi-modal intelligent channel modeling (MMICM) framework ... Inspired by human synesthesia, we conceptualize multi-modal sensors and communication devices as an agent's 'sensory organs' and artificial neural networks as its 'brain', proposing Synesthesia of Machines (SoM) [7]. ... and proposed a novel MMICM framework for 6G-enabled networked intelligent systems. [Ref. 6:] L. Bai et al., 'Multi-modal intelligent channel modeling: A new modeling paradigm via synesthesia of machines,' IEEE Commun. Surveys & Tutorials, 2025."

The central concept the paper presents as novel, MMICM, is the same paradigm already published by the same group in [6], titled 'Multi-modal intelligent channel modeling: A new modeling paradigm via synesthesia of machines.' Its foundational notion, SoM, is attributed to [7], also authored by the present group. The paper supplies no independent derivation, external dataset, or benchmark to break the loop: the novelty claim reduces to 'we proposed this before,' and the framework's validity is supported by the authors' own prior publications rather than by evidence presented here. This is load-bearing because the entire framework—input module, output module, mapping, and applications—is organized around this self-cited paradigm.

full rationale

The manuscript is a conceptual framework paper with no equations, so the strongest equation-level circularity patterns (self-definitional, fitted-input-called-prediction) cannot be exhibited. The main circularity concern is a load-bearing self-citation chain: SoM is introduced via [7], MMICM is effectively the same paradigm as [6], and the proposed dataset direction cites the authors' own [8]. The paper's assertions of 'novel' and 'unprecedented capability' are not backed by training details, held-out experiments, or quantitative metrics; Figures 4 and 5 are qualitative and the paper itself concedes in Section V that 'current MMICM outputs are limited to partial estimations and statistical descriptions' and 'generalization remains limited by data-driven constraints.' These are validity/support problems rather than circular reductions. Because the central claim does not reduce to a fit or to a definition by construction, a score of 4 is appropriate: substantial self-citation underpins the framework's identity and novelty, but the core mapping hypothesis still has independent conceptual content and is not shown to be equivalent to its inputs.

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

The paper contributes an architecture proposal, not a derivation. It introduces no fitted parameters, but rests on several unproved domain assumptions: the sufficiency of multi-modal sensing for channel inference, cross-frequency and cross-scenario generalization of a learned mapping, attainability of fine sensor alignment, and the reliability of unstated ground-truth data. The framework itself (MMICM) and the pathloss-image capability are proposed constructs with no falsifiable handle inside the paper; they inherit from the authors' prior works [6], [7], and [8].

assumptions (4)

domain assumption Multi-modal sensing data (RGB, depth, LiDAR, radar) carries sufficient information to infer electromagnetic channel characteristics.
Core premise of MMICM, stated in Sec. III and throughout the paper; no proof or empirical evidence is offered.
domain assumption A single trained model can generalize across scenarios, frequency bands (sub-6 GHz to THz), and mobility regimes.
Claimed as 'extension capabilities' in Sec. II-C and Sec. III; it is the framework's key selling point and is unsupported by any experiment.
domain assumption Precise spatio-temporal synchronization (sub-millisecond timing, centimeter-level registration) of heterogeneous sensors is achievable in target deployments.
Sec. III-A states this as the foremost technical priority, citing the authors' own SynthSoM dataset [8].
domain assumption The 'ground truth' channels used in the qualitative examples (Figs. 4-5) come from reliable measurements or simulations.
The source data and comparison methodology for Figs. 4-5 are not described anywhere in the paper.

invented entities (2)

MMICM framework
purpose: Proposed architecture that maps multi-modal sensing data to large-scale and small-scale channel characteristics.
No falsifiable quantitative prediction is provided; the two illustrative examples are qualitative and the paradigm derives from the authors' prior work [6].
Pathloss image prediction capability
purpose: Predict a spatial field of point-to-point pathloss values at few-meter resolution from a single sensing image.
Claimed as 'unprecedented' in Sec. III-B.1, but no implementation, dataset, or metric is given.

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Cite this review

Pith. "Pith review of Multi-Modal Intelligent Channel Modeling Framework for 6G-Enabled Networked Intelligent Systems." pith.science (2026). https://pith.science/paper/UL2WFBQA

@misc{pith2026250907422,
  author       = {Pith},
  title        = {Pith review of: Multi-Modal Intelligent Channel Modeling Framework for 6G-Enabled Networked Intelligent Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UL2WFBQA}},
  note         = {Machine review of arXiv:2509.07422}
}

read the original abstract

The design and technology development of 6G-enabled networked intelligent systems needs an accurate real-time channel model as the cornerstone. However, with the new requirements of 6G-enabled networked intelligent systems, the conventional channel modeling methods face many limitations. Fortunately, the multi-modal sensors equipped on the intelligent agents bring timely opportunities, i.e., the intelligent integration and mutually beneficial mechanism between communications and multi-modal sensing could be investigated based on the artificial intelligence (AI) technologies. In this case, the mapping relationship between physical environment and electromagnetic channel could be explored via Synesthesia of Machines (SoM). This article presents a novel multi-modal intelligent channel modeling (MMICM) framework for 6G-enabled networked intelligent systems, which establishes a nonlinear model between multi-modal sensing and channel characteristics, including large-scale and small-scale channel characteristics. The architecture and features of proposed intelligent modeling framework are expounded and the key technologies involved are also analyzed. Finally, the system-engaged applications and potential research directions of MMICM framework are outlined.

Figures

Figures reproduced from arXiv: 2509.07422 by the authors.

**Figure 2.** The multi-modal sensing-communication datasets. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** The overall structure of proposed MMICM framework. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: The RGB image captured by the UAV serves as the input to infer the [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: In the V2V scenario, LiDAR sensors mounted on transceivers capture the surrounding environment as input for scatterer generation (left). The 3D [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: Highly system-engaged applications of proposed MMICM framework. [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages

[6]

Multi-modal intelligent channel modeling: A new model- ing paradigm via synesthesia of machines,

L. Baiet al., “Multi-modal intelligent channel modeling: A new model- ing paradigm via synesthesia of machines,”IEEE Commun. Surveys & Tutorials, 2025. DOI: 10.1109/COMST.2025.3558046

work page arXiv 2025
[7]

Intelligent multi-modal sensing-communication inte- gration: Synesthesia of Machines,

X. Chenget al., “Intelligent multi-modal sensing-communication inte- gration: Synesthesia of Machines,”IEEE Commun. Surveys & Tutorials, vol. 26, no. 1, pp. 258–301, first—quarter 2024

work page 2024
[8]

SynthSoM: A synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM)

X. Chenget al., “SynthSoM: A synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM),” Sci. Data, 2025. [Online]. Available: https://arxiv.org/abs/2501.07459

work page Pith review arXiv 2025
[1]

6G enabled advanced transportation systems,

R. Liuet al., “6G enabled advanced transportation systems,”IEEE Trans. Intelligent Transportation Systems, vol. 25, no. 9, pp. 10564–10580, Sept. 2024

work page 2024
[2]

Molisch,Wireless Communications

A. Molisch,Wireless Communications. UK: John Wiley Sons, 2011

work page 2011
[3]

An overview of machine learning techniques for radiowave propagation modeling,

A. Seretis and C. D. Sarris, “An overview of machine learning techniques for radiowave propagation modeling,”IEEE Trans. Antennas Propag., vol. 70, no. 6, pp. 3970—3985, June 2022. 9

work page 2022
[4]

A survey of 5G channel measurements and models,

C.-X. Wanget al., “A survey of 5G channel measurements and models,” IEEE Commun. Surveys & Tutorials, vol. 20, no. 4, pp. 3142—3168, Fourth–quarter 2018

work page 2018
[5]

AI-enabled data-driven channel modeling for future communications,

M. Yanget al., “AI-enabled data-driven channel modeling for future communications,”IEEE Commun. Mag., vol. 62, no. 4, pp. 112–118, Apr. 2024

work page 2024

Show all 15 references

[9]

Sensing aided OTFS massive MIMO systems Compressive channel estimation,

S. Jiang and A. Alkhateeb, “Sensing aided OTFS massive MIMO systems Compressive channel estimation,” inProc. IEEE Int. Conf. Commun. Workshops (ICC Workshops), Rome, Italy, Jun. 2023, pp. 794—799

2023
[10]

Soft handover procedures in mmWave cell-free massive MIMO networks,

M. Zaher, E. Bjornson, and M. Petrova, “Soft handover procedures in mmWave cell-free massive MIMO networks,”IEEE Trans. Wireless Commun., vol. 23, no. 6, pp. 6124—6138, Jun. 2024

2024
[11]

Model predictive path planning of AGVs Mixed logical dynamical formulation and distributed coordination,

J. Xin,et al., “Model predictive path planning of AGVs Mixed logical dynamical formulation and distributed coordination,”IEEE Trans. Intell. Transp. Syst., vol. 24, no. 7, pp. 6943-–6954, Jul. 2023

2023
[12]

Radio map assisted path planning for UA V anti-jamming communications,

Y . Dong, C. He, Z. Wang, and L. Zhang, “Radio map assisted path planning for UA V anti-jamming communications,”IEEE Signal Process Lett., vol. 29, pp. 607—611, Feb. 2022

2022
[13]

Collaborative fine-tuning of mobile AIGC models with wireless channel conditions,

H. Wang, H. Li, M. Sheng, and J. Li, “Collaborative fine-tuning of mobile AIGC models with wireless channel conditions,”IEEE Wireless Commun., vol. 31, no. 4, pp. 32–38, Aug. 2024

2024
[14]

Real-time digital twins Vision and research directions for 6G and beyond,

A. Alkhateeb, S. Jiang, and G. Charan, “Real-time digital twins Vision and research directions for 6G and beyond,”IEEE Commun. Mag., vol.61, no. 11, pp. 128-–134, Nov. 2023

2023
[15]

Embodied intelligence via learning and evolution,

A. Gupta, S. Savarese, S. Ganguli, and F. Li, “Embodied intelligence via learning and evolution,”Nat. Commun., vol. 12, pp. 5721—5732, Oct. 2021

2021

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.

[1] [6]

Multi-modal intelligent channel modeling: A new model- ing paradigm via synesthesia of machines,

L. Baiet al., “Multi-modal intelligent channel modeling: A new model- ing paradigm via synesthesia of machines,”IEEE Commun. Surveys & Tutorials, 2025. DOI: 10.1109/COMST.2025.3558046

work page arXiv 2025

[2] [7]

Intelligent multi-modal sensing-communication inte- gration: Synesthesia of Machines,

X. Chenget al., “Intelligent multi-modal sensing-communication inte- gration: Synesthesia of Machines,”IEEE Commun. Surveys & Tutorials, vol. 26, no. 1, pp. 258–301, first—quarter 2024

work page 2024

[3] [8]

SynthSoM: A synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM)

X. Chenget al., “SynthSoM: A synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM),” Sci. Data, 2025. [Online]. Available: https://arxiv.org/abs/2501.07459

work page Pith review arXiv 2025

[4] [1]

6G enabled advanced transportation systems,

R. Liuet al., “6G enabled advanced transportation systems,”IEEE Trans. Intelligent Transportation Systems, vol. 25, no. 9, pp. 10564–10580, Sept. 2024

work page 2024

[5] [2]

Molisch,Wireless Communications

A. Molisch,Wireless Communications. UK: John Wiley Sons, 2011

work page 2011

[6] [3]

An overview of machine learning techniques for radiowave propagation modeling,

A. Seretis and C. D. Sarris, “An overview of machine learning techniques for radiowave propagation modeling,”IEEE Trans. Antennas Propag., vol. 70, no. 6, pp. 3970—3985, June 2022. 9

work page 2022

[7] [4]

A survey of 5G channel measurements and models,

C.-X. Wanget al., “A survey of 5G channel measurements and models,” IEEE Commun. Surveys & Tutorials, vol. 20, no. 4, pp. 3142—3168, Fourth–quarter 2018

work page 2018

[8] [5]

AI-enabled data-driven channel modeling for future communications,

M. Yanget al., “AI-enabled data-driven channel modeling for future communications,”IEEE Commun. Mag., vol. 62, no. 4, pp. 112–118, Apr. 2024

work page 2024