6G-Enabled Smart Railways

Bo Ai; Dusit Niyato; Guoyu Ma; Jiayi Zhang; Ruisi He; Wei Chen; Yong Niu; Yuguang Fang; Yunlong Lu; Zhangdui Zhong

REVIEW 5 major objections 6 minor 2 cited by

6G-Enabled Smart Railways

T0 review · 5 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Future smart railways will need a 6G network that combines communication, computing, edge intelligence, and caching to reach speeds above 1000 km/h with centimeter-level positioning

desk verdict Useful map of 6G railway research, but it is a survey wearing a research paper's clothes: the headline capabilities are asserted, not shown, and the 'proposed' mechanisms are mostly the authors' own earlier results. read the letter →

arxiv 2505.12946 v1 pith:H4MZ5TOI submitted 2025-05-19 eess.SY cs.SY

Bo Ai , Yunlong Lu , Yuguang Fang , Dusit Niyato , Ruisi He , Wei Chen , Jiayi Zhang , Guoyu Ma

show 2 more authors

Yong Niu Zhangdui Zhong

This is my paper · ORCID

classification eess.SYcs.SY

keywords 6GsmartrailwaysintegratedcommunicationscomputingcachingOTFS-TSMAcross-domainchannelmodelingreconfigurableintelligentsurfacessensingandendogenoussecurityedgeintelligence

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 argues that future smart railways will demand performance that 5G-R cannot provide: train speeds above 1000 km/h, 1 ms latency, 99.99999% reliability, full coverage, and centimeter-level positioning. Its central proposal is an integrated network architecture that pools communications, computing, edge intelligence, and caching, organized into user, edge, and core layers connected through terrestrial, aerial, and space networks. On top of that architecture, the paper identifies and evaluates the enabling technologies that would carry the requirements: AI-assisted cross-domain channel modeling, OTFS-TSMA for massive access under Doppler, RIS-assisted coverage, THz and vacuum-tube links, integrated sensing and communications, and endogenous security built on blockchain and federated learning. A sympathetic reading takes the paper as a roadmap: if these mechanisms are realized together, autonomous, full-coverage, secure high-speed rail becomes a concrete 6G use case rather than an aspiration.

What carries the argument

The load-bearing object is the integrated 6G railway network architecture: a three-layer (user, edge, core) structure embedded in space-air-ground integrated networks, designed so that communications, computing, caching, and edge intelligence share resources. The named transmission mechanism carrying the massive-access argument is OTFS-TSMA (orthogonal time-frequency-space modulation enabled tandem spreading multiple access), which gives each user a unique tandem spreading combination as identity and exploits the two-dimensional convolution structure of OTFS to recover collisions. Secondary mechanisms doing specific work are the AI satellite-image-to-path-loss model, RIS phase optimization for high-speed channels, and the THz location-aware scheduling heuristic.

What would settle it

Run a 5G-R or 5G-Advanced system on a high-speed test line and measure whether it delivers 1 ms latency, 99.99999% reliability, centimeter-level positioning, and stable handover at 1000 km/h; meeting those targets would falsify the paper's premise that a 6G-specific architecture and mechanisms are necessary.

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Extended reading notes

Core claim

The paper's central claim is that the gap between 5G-R and future smart railways is architectural, not incremental: 5G's own indicators cap mobility at 500 km/h and do not define safety or positioning accuracy, while 6G railway targets include speeds above 1000 km/h, 1 ms latency, 99.99999% reliability, and centimeter-level positioning. To close that gap, the paper proposes and justifies an integrated 6G railway network architecture and a set of key enabling technologies. The architecture jointly optimizes communications, computing, caching, and edge intelligence across user, edge, and core layers, connected through terrestrial, aerial, and space networks. The technology claims include an AI-enabled cross-domain channel model that predicts path loss from satellite images with 6.36 dB RMSE; an OTFS-TSMA scheme that gives massive user access under high Doppler; RIS configurations that mitigate channel aging; THz scheduling for vacuum-tube trains; and an endogenous security framework built on blockchain, zero-knowledge proofs, and federated learning.

Load-bearing premise

Everything rests on the claim that the targets future railways set, over 1000 km/h, 1 ms latency, 99.99999% reliability, and centimeter-level positioning, cannot be met by continued 5G evolution; if 5G-R with enhancements reaches them, the case for a 6G-specific railway architecture loses its foundation.

Editorial extensions

If this is right

If the architecture is right, a 6G railway network can serve ultra-high-speed operation (above 1000 km/h) and full-terrain coverage simultaneously, with safety and positioning handled at the architecture level rather than patched on later.
OTFS-TSMA would let a spectrum-limited railway cell serve hundreds of devices at high mobility (the paper reports 560 users) without orthogonal pilot assignment, a scale that 5G orthogonal access cannot reach.
The AI channel model makes track-corridor path-loss prediction possible from satellite imagery, which could turn fixed rail routes into precomputed radio maps for beam selection and handover decisions.
RIS deployment on train windows or tracksides can reduce Doppler-induced channel aging and spectral-efficiency loss, lowering the antenna count and energy cost at base stations.
Endogenous security with blockchain and zero-knowledge proofs could authenticate federated-learning updates and train-control messages while preserving privacy, a prerequisite for fully automatic rail operations.

Reading between the lines

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

Implicit in the paper but not developed: if the measured HSR channel spreading function is less sparse than ideal tapped-delay-line models assume, then current OTFS receiver designs, including parts of OTFS-TSMA, will need reworking; the paper flags this but leaves the redesign to later work.
The requirement targets (1000 km/h, 1 ms, 99.99999%, centimeter-level positioning) come from 6G vision documents rather than from a quantitative comparison with 5G-R, so a natural extension is to benchmark 5G-Advanced with railway enhancements against those same targets.
The AI satellite-image path-loss approach could be extended to tunnels, viaducts, and cuttings by fusing LiDAR or point-cloud data when satellite imagery is occluded, a direction the paper notes only as a limitation.
The THz vacuum-tube scheduling formulation is an NP-hard link-scheduling problem, so the same location-aware heuristic logic could transfer to other linear high-speed corridors such as maglev or hyperloop test tracks.

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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

Quick take: this is a competent, wide-ranging survey of 6G technologies for railways, and it will be handy for anyone needing an entry point to the area. It is not, despite the abstract, a demonstrated design for meeting speeds above 1000 km/h, 1 ms latency, 10^-7 reliability, or centimeter-level positioning. Treat it as a roadmap with an inflated frame.

The genuinely useful parts: the comparison with prior surveys (Table I), the organization of the field into channel modeling, coverage, reliable transmission, edge intelligence, security, and IoT, and the concrete identification of gaps (T2T measurements at high speed, mmWave channel campaigns, discrete channel spreading function behavior in OTFS). The railway-specific angle—track-bound mobility, predictable handovers, tunnels, and vacuum-tube maglev—is a real differentiator from generic 6G surveys. The authors know the literature; the citation density is appropriate for a survey.

The soft spots are proportional. First, the originality is overstated. OTFS-TSMA, ADJSCC-CSI, BSAMP-CP, and the multi-task federated learning results all come from the authors' prior publications, yet the contribution list and abstract present them as new proposals here. The right fix is transparent labeling: say 'we review our prior mechanism X and summarize its results.' Second, the headline KPIs are never checked. The stress-test note is correct: no simulation or link budget ties the proposed architecture to 1 ms latency, 10^-7 reliability, or centimeter-level positioning. The positioning requirement appears as a target, not a result. Third, the modeling sections are thin. The onboard-RIS channel model in Eq. (1)-(2) is stated without derivation, and the simulation figures from prior work have no code, data, or error bars. There is also a typo ('107 devices' should be 10^7) that should be caught. None of this sinks the survey's value as a survey, but it means the paper cannot support the stronger claims made in the abstract.

Who is this for? Someone starting in railway communications, or a researcher who wants a quick map of 6G railway topics and references. It deserves peer review at a journal that handles surveys, but only with major revision: reframe as a survey with clearly separated prior contributions, soften the claims about meeting KPIs, and either provide derivations and artifacts or drop the word 'proposed' for inherited results.

Referee Report

5 major / 6 minor

Summary. The paper is a broad survey and position paper on 6G-enabled smart railways. It argues that 5G and 5G-R cannot meet future railway requirements such as speeds above 1000 km/h, 1 ms latency, 99.99999% reliability, full coverage, centimeter-level positioning, and ultra-high security, and that an integrated 6G architecture combining communications, computing, edge intelligence, caching, and endogenous security is needed. The paper reviews and categorizes a large body of work on network architecture, cross-domain channel modeling, RIS, cell-free massive MIMO, OTFS, THz communications, semantic communication, edge intelligence, security and privacy, digital twins, mMTC, and ISAC. It also includes a number of 'proposed' mechanisms, mostly drawn from the authors' prior publications, with selected simulation results reproduced or summarized. The final sections discuss future research directions.

Significance. If the central claim were established, the paper would be a useful one-stop reference for 6G railway research: it covers an unusually wide span of topics, connects them to railway-specific constraints, and includes several concrete measurements and performance comparisons, such as the HSR channel spreading function characterization in Section IV.A.2 and the mMTC data-recovery comparisons in Section VI.A. The strengths are the breadth of the literature coverage, the railway-specific framing of generic 6G technologies, and the inclusion of some real measurement data rather than simulations alone. However, the paper's most load-bearing claim, that the described architecture and enabling technologies 'can support' the stated KPIs, is asserted rather than demonstrated: the component-level results are not connected to the headline 1 ms latency, 10^-7 reliability, or centimeter-level positioning targets. As a survey the paper is informative, but as a proposal of new mechanisms it is under-specified and largely self-referential, so the current version needs substantial revision before its central claim can be accepted.

major comments (5)

[Abstract and Section I.B] The paper's central claim, that the proposed 6G architecture and enabling technologies can support speeds above 1000 km/h, 1 ms latency, 99.99999% reliability, and centimeter-level positioning, is not established by any quantitative evidence in the manuscript. The component simulations are not tied to these KPIs: Section IV.A reports BER for OTFS-TSMA with 560 users, Section IV.B reports scheduling slots and throughput, and Section VI.A reports data-recovery ratios, but none of these results is converted into end-to-end latency, reliability at the 10^-7 level, or positioning error. The centimeter-level positioning requirement appears only as a target; no positioning algorithm or accuracy result is presented. Because the entire motivation rests on the claim that 5G-R cannot meet these requirements and that the proposed 6G mechanisms can, this gap is load-bearing. The authors should either add a quantitative feasibility assessment, such as a link budget or an end-to-end system-level simulation, or explicitly reframe the paper as a survey of candidate technologies without claiming that the KPIs are met.
[Section III.B.6, Eqs. (1)-(2)] The onboard RIS-assisted channel model in Eq. (1) and the phase optimization objective in Eq. (2) are presented as the paper's own proposal, but the manuscript does not define the constituent terms h_SBR, h_MBR, h_LoS, h_SB, h_MB, the RIS phase model, or the statistical expectations, and no derivation or validation is provided. Figure 5 shows a simulation result, but the simulation setup, parameters, and comparison with measurement are absent. This makes the 'proposed' RIS channel model unverifiable from the manuscript. The authors should either supply the complete model definitions and validation or clearly attribute this material to a prior publication and state what is new here.
[Section IV.A.1 and Section IV.C] Several mechanisms are presented in the contributions list and in the body as if they were new proposals of this paper, yet the text itself attributes them to earlier publications: OTFS-TSMA 'was proposed in [120]', ADJSCC-CSI 'was proposed in [176]', and the BSAMP-CP algorithm 'is proposed in [225]'. The same pattern appears for the multi-task federated learning results in Section V.A, which cite Refs. [186]-[187]. This creates a circularity problem: the paper claims novelty for mechanisms that are only summarized from the authors' prior work, and the simulations shown are those earlier results. The authors should clearly distinguish survey content from new contributions, state the incremental contribution of this manuscript, and ensure that any reproduced figures are properly attributed.
[Section IV.B, Eqs. (4)-(10)] The THz communication scheduling problem is not fully specified. In Eq. (4), the terms P_r^THz(i) and I_ji^THz are not defined, and the text does not state how they are obtained from the channel model or link budget; Eq. (7) defines q_a^l without ever defining q_l or the relation between q_l and the flow QoS requirement q_i; and the parameters Delta-T, T_s, and M in Eqs. (7) and (10) are used without definitions. More importantly, the simulations in Figs. 17-18 are not accompanied by a THz channel model, antenna pattern, molecular absorption model, or link budget, so the reader cannot judge whether the reported scheduling gains are an artifact of the simulation assumptions. The authors should complete the model and simulation specification, or remove the quantitative claims and present the scheduling formulation as a problem statement only.
[Section V.A.3] The presentation of the parameter-authentication federated learning (PAFL) scheme is incomplete where it matters. After the sentence 'With the abnormal local models, the global model is updated as:', the expected equation is missing, and the subsequent protocol description leaves undefined how the zkSNARK proof π_v is constructed, what statement it proves, and how the Pedersen commitment interacts with the zero-knowledge proof. Figure 26 reports an accuracy comparison without stating the simulation setup, dataset, attack model, or baseline configurations. Since PAFL is presented as one of the paper's proposed contributions, this missing technical content prevents the reader from assessing the scheme. The authors should provide the omitted update equation, a complete protocol description with security assumptions, and the simulation configuration.

minor comments (6)

[Section IV.A] The phrase '107 devices per kilometer' should read '10^7 devices per kilometer'; the same superscript formatting problem appears in Section VI.A for '106 to 107 devices per square kilometer'.
[Section V.A.2] In the sentence defining τ_mivit, the text says 'τ_Comm is the transmission delay and τ_Comm is the computing delay'; the second occurrence should be τ_Comp to match Eq. (15).
[Table I] The last row of Table I is labeled '[11], 2018', which duplicates the first row; the row describing the IoT solution should cite Ref. [16] instead.
[Section IV.A.2] The text refers to 'Fig. 16(b)' when comparing BER under different user activation probabilities; this should be 'Fig. 15(b)'.
[Section IV.B] The phrase 'EFH frequency band' should be 'EHF frequency band', and the wavelength range given ('1 cm to 1 mm') corresponds to EHF, not to the THz band described immediately afterward; the terminology should be corrected.
[Section II.H.1 and Section II.H.3] Minor typos: 'conventional neural network' should be 'convolutional neural network', 'below 5GH' should be 'below 5 GHz', and 'fasting fading' in Section III.B.2 should be 'fast fading'.

Circularity Check

1 steps flagged · score 2.0 of 10

No significant circularity; the only issue is a mild self-citation presentation where a component proposed in the abstract is credited to the authors' own prior work in the body.

other [Abstract; Section IV.A.1 (OTFS-TSMA) and Fig. 15 discussion]
"For high-speed mobile scenarios, we propose an AI-enabled cross-domain channel modeling and orthogonal time-frequency space-time spread multiple access mechanism to alleviate the conflict between limited spectrum availability and massive user access. ... To realize massive connections with high reliability and low complexity for umMTC in smart railways, OTFS-TSMA was proposed in [120] ... The performance of OTFS-TSMA in terms of bit error rate (BER) is demonstrated in Fig. 15. Based on the proposed tandem spreading combinations in OTFS-TSMA, the system can access 560 users [120]."

The abstract presents OTFS-TSMA as a new proposal of this paper, while Section IV.A.1 attributes it to [120], a prior work by overlapping authors, and the specific capability of accessing 560 users is taken from [120] without re-derivation here. This makes the claimed contribution reduce, in part, to a self-citation rather than a new result. The effect is limited because the paper is primarily a survey and the overall 6G-railway architecture does not rest on this single component; the central KPI claims are asserted from external vision documents, not derived from this citation.

full rationale

The paper is a survey and architecture-proposal document rather than a chain of derivations. Its core claim that 6G can satisfy future smart-railway requirements is supported by citing external 6G vision documents (Hexa-X, Rail Route 2050, Finnish 6G program) and by qualitative architectural reasoning; it is not derived from the authors' own equations. The quantitative component results (OTFS-TSMA BER, RIS spectral efficiency, BSAMP-CP data recovery, multi-task FL convergence) are either reproduced or cited from the authors' earlier papers, which is self-citation but not a case of fitting a parameter to data and then calling the same quantity a prediction. The RIS channel-model discussion (Eq. (1)) does contain a result that is largely a consequence of the model definition--more RIS elements increase the SBR component and thus the CIR magnitude--but this is presented as a model-based observation, not as an empirical or first-principles prediction, so it does not constitute circularity in the sense of a claimed derivation reducing to its own input. No uniqueness theorem, ansatz-smuggling via citation, or renaming of a known result as a new contribution was found beyond the attribution inconsistency noted above. Accordingly, the circularity score is low.

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

The central contribution is a survey, so no new physical entities or fitted constants are introduced. The paper's original frameworks (OTFS-TSMA, PAFL, onboard RIS model) are primarily descriptions of the authors' prior work, which serve as inputs rather than newly derived results.

free parameters (2)

AI path-loss model weights = Trained on open-source 5.9 GHz data; test RMSE 6.36 dB
The neural network weights are fitted to the channel measurement dataset; the reported RMSE is a test-set fit, not a parameter-free prediction, and is presented as evidence in Section III.B.8.
THz scheduling simulation parameters = 24 mobile relays, QoS 10-500 Mbps, fTHz = 340 GHz
These values are chosen for the simulation in Section IV.B and affect the reported performance; they are not derived from railway standards or measurements.

assumptions (4)

domain assumption Future smart railway requirements (speeds > 1000 km/h, 1 ms latency, 99.99999% reliability, centimeter-level positioning) cannot be met by 5G evolution alone.
Asserted in Section I.B as the motivation for the paper; it is a forward-looking assumption from 6G vision documents (Hexa-X, Rail Route 2050) and is not established by the paper itself.
domain assumption 6G technologies (RIS, THz, OTFS, ISAC, blockchain, digital twins) will mature and can be integrated into railway networks.
The proposed architecture assumes these technologies will become deployable; the survey highlights challenges but does not prove feasibility.
standard math Standard wireless channel and information-theoretic models used in cited references are valid for railway scenarios.
The paper relies on Shannon capacity, fading models, compressed sensing, and OTFS models from the literature without re-deriving them.
domain assumption The measured channel spreading function from the Beijing-Shenyang railway ([154]) is representative of high-speed railway channels.
The discussion of OTFS-TSMA in Section IV.A uses this measurement to characterize practical channels, but only one measurement campaign is cited.

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Cite this review

Pith. "Pith review of 6G-Enabled Smart Railways." pith.science (2026). https://pith.science/paper/H4MZ5TOI

@misc{pith2026250512946,
  author       = {Pith},
  title        = {Pith review of: 6G-Enabled Smart Railways},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/H4MZ5TOI}},
  note         = {Machine review of arXiv:2505.12946}
}

read the original abstract

Smart railways integrate advanced information technologies into railway operating systems to improve efficiency and reliability. Although the development of 5G has enhanced railway services, future smart railways require ultra-high speeds, ultra-low latency, ultra-high security, full coverage, and ultra-high positioning accuracy, which 5G cannot fully meet. Therefore, 6G is envisioned to provide green and efficient all-day operations, strong information security, fully automatic driving, and low-cost intelligent maintenance. To achieve these requirements, we propose an integrated network architecture leveraging communications, computing, edge intelligence, and caching in railway systems. We have conducted in-depth investigations on key enabling technologies for reliable transmissions and wireless coverage. For high-speed mobile scenarios, we propose an AI-enabled cross-domain channel modeling and orthogonal time-frequency space-time spread multiple access mechanism to alleviate the conflict between limited spectrum availability and massive user access. The roles of blockchain, edge intelligence, and privacy technologies in endogenously secure rail communications are also evaluated. We further explore the application of emerging paradigms such as integrated sensing and communications, AI-assisted Internet of Things, semantic communications, and digital twin networks for railway maintenance, monitoring, prediction, and accident warning. Finally, possible future research and development directions are discussed.

Figures

Figures reproduced from arXiv: 2505.12946 by the authors.

**Figure 2.** Network architecture of 6G smart railways [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗

**Figure 3.** Different views of 6G railway architecture [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Figures from the paper (30 more)

**Figure 5.** Figure 5: Absolute envelope magnitude of CIR using RIS [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]

**Figure 4.** Figure 4: On-board RIS-assisted high-speed train channels. [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]

**Figure 8.** Figure 8: RMSE box line graph comparison C. RIS for High-Mobility Scenarios [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]

**Figure 6.** Figure 6: The proposed model architecture The model achieves a root mean square error (RMSE) of 6.36dB on the test set, with a 2.5dB relative improvement in RMSE when compared to a model trained using only satellite images of the area near the receiver. The prediction results of…

**Figure 7.** Figure 7: Comparison of model results under NLOS conditions [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]

**Figure 11.** Figure 11: Average SE versus time instant [PITH_FULL_IMAGE:figures/full_fig_p016_11.png]

**Figure 12.** Figure 12: show the effect of the channel aging on the spectral efficiency (SE). We use the normalized Doppler shift fDTs to represent the impact of channel aging on system performance. The larger the value of fDTs, i.e., the faster the users move, the more seriously the impact …

**Figure 13.** Figure 13: OTFS-TSMA transmission diagram. (a) (b) [PITH_FULL_IMAGE:figures/full_fig_p018_13.png]

**Figure 14.** Figure 14: Channel spreading function of (a) TDL channel model and (b) practical HSR scenario measured in [155]. [PITH_FULL_IMAGE:figures/full_fig_p018_14.png]

**Figure 15.** Figure 15: Performance of OTFS-TSMA versus TSMA and OFDM-TSMA in terrestrial mMTC for smart railways: (a) BER [PITH_FULL_IMAGE:figures/full_fig_p019_15.png]

**Figure 16.** Figure 16: HSR communication system in the vacuum tube. [PITH_FULL_IMAGE:figures/full_fig_p020_16.png]

**Figure 17.** Figure 17: Number of time slots versus different numbers of [PITH_FULL_IMAGE:figures/full_fig_p021_17.png]

**Figure 18.** Figure 18: System throughput versus numbers of flows. [PITH_FULL_IMAGE:figures/full_fig_p021_18.png]

**Figure 19.** Figure 19: The detailed architecture of Rail-SC it introduces significant overhead. Furthermore, continuously monitoring time-varying channels for model switching is challenging, which can reduce the accuracy of CSI reconstruction and, consequently, degrade the system’s communi…

**Figure 20.** Figure 20: The framework of ADJSCC-CSI In traditional modular communication systems, DL-based networks are often designed independently to perform specific functions and replace corresponding modules in classical communication systems. However, the optimization spaces of differ…

**Figure 21.** Figure 21: The framework of hierarchy-aware and channel-adaptive data fusion In Fig.21, a hierarchy-aware and channel-adaptive semantic communication framework is proposed [177]. This framework integrates semantic communication techniques with multimodal data fusion, enabling t…

**Figure 22.** Figure 22: Edge intelligence network architecture • Dynamic channel scenarios for T2G communication: To address the dynamic and time-varying channel scenarios in T2G communication, a machine learning-based channel prediction model is trained in real-time onboard MEC. In additio…

**Figure 23.** Figure 23: A multi-task FL model for railway networks [PITH_FULL_IMAGE:figures/full_fig_p024_23.png]

**Figure 24.** Figure 24: Convergence of the multi-task FL in different algorithms [PITH_FULL_IMAGE:figures/full_fig_p024_24.png]

**Figure 25.** Figure 25: Delay of multi-task FL as the user coalition changes [PITH_FULL_IMAGE:figures/full_fig_p025_25.png]

**Figure 26.** Figure 26: Accuracy of FL under defensive schemes prioritize both built-in security and intelligence, while integrating technologies such as blockchain and digital twins [189]. 1) Endogenous secure railway network architecture: The current 5G network adheres to the traditional …

**Figure 27.** Figure 27: The network integrates defense mechanisms from the [PITH_FULL_IMAGE:figures/full_fig_p027_27.png]

**Figure 28.** Figure 28: Blockchain-based zero trust architecture. [PITH_FULL_IMAGE:figures/full_fig_p028_28.png]

**Figure 29.** Figure 29: Digital twin networks DT has also attracted wide attention in related fields of rail transportation, including intelligent maintenance [211], smart stations [212], as well as operational fault detection and diagnosis, and environmental monitoring [213]. For example, …

**Figure 30.** Figure 30: A hierarchical architecture of digital twin networks. [PITH_FULL_IMAGE:figures/full_fig_p030_30.png]

**Figure 32.** Figure 32: The process begins with BSs training local models on [PITH_FULL_IMAGE:figures/full_fig_p030_32.png]

**Figure 33.** Figure 33: The processes of constructing a digital twin [PITH_FULL_IMAGE:figures/full_fig_p031_33.png]

**Figure 34.** Figure 34: Railway access scenario when a train passing by. [PITH_FULL_IMAGE:figures/full_fig_p033_34.png]

**Figure 36.** Figure 36: Comparison of data recovery ratio for varying SNRs. [PITH_FULL_IMAGE:figures/full_fig_p034_36.png]

**Figure 37.** Figure 37: Integrated Sensing and Communication However, the integrated design of sensing, communications, computing, storage, and intelligence (SCCSI) in railway scenarios still has three key challenges: 1) Integrated Design of Sensing, Communication, Computing, and Intellige…

Discussion (0). Continue with ORCID to comment.

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