Pith. sign in

REVIEW 3 major objections 5 minor 177 references

Network Digital Twin for 6G and Beyond: An End-to-End View Across Multi-Domain Network Ecosystems

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper claims to be the first comprehensive end-to-end survey of network digital twins for 6G, organizing the field across RAN, transport, core, cloud/edge, applications, non-terrestrial networks, and quantum networking.

desk verdict A useful but unfinished 6G digital-twin survey: the scope is genuinely broader than prior surveys, but duplicated text and broken citation mappings make the current version unreliable as an entry point. read the letter →

arxiv 2506.01609 v1 pith:S7NREAGA submitted 2025-06-02 cs.NI eess.SP

Dinh-Hieu Tran , Nazar Waheed , Yuris Mulya Saputra , Xingqin Lin , Cong T. Nguyen , Tedros Salih Abdu , Van Nhan Vo , Van-Quan Pham
show 7 more authors
Madyan Alsenwi Abuzar Babikir Mohammad Adam Symeon Chatzinotas Eva Lagaunas Hung Tran Tu Ho Dac Nguyen Van Huynh
This is my paper
classification cs.NIeess.SP
keywords networkdigitaltwin6Gnetworksradioaccess5Gcorecloudandedgecomputingnon-terrestrialquantumnetworkingsurvey
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 sets out to establish that network digital twins (NDTs) — live virtual replicas of a physical network, continuously updated with real-time data and able to feed validated decisions back — are the unifying tool for designing, testing, and operating 6G and beyond networks. It claims to be the first survey to review NDT applications across the full network stack: radio access networks (including O-RAN and intelligent surfaces), transport networks, 5G core and beyond (5GCORE+), cloud/edge computing, vertical applications (blockchain, health, manufacturing, security, vehicular networks), non-terrestrial networks, and quantum networking, from both academic and industrial viewpoints. Within this, the authors specifically claim the first in-depth guide to using digital twins in RAN and 5GCORE+. If the survey's map is accurate, a newcomer can use it as the entry point to the field, and operators can use the collected use cases to decide where a twin earns its keep before deploying it.

What carries the argument

The central object is the network digital twin itself, defined as a continuously updated virtual replica of a physical network whose four required elements are data, models, mapping, and interfaces. The survey's organizing device is the end-to-end layered architecture (its Fig. 2) that stacks twins for RAN/O-RAN, transport, 5G core, cloud/edge, applications, NTN, and quantum networks, and the survey uses that layer stack to sort and compare primary works. The recurring mechanism inside each layer is a closed loop: real-time telemetry from the physical network feeds the twin, the twin runs physics-based simulation and AI/ML what-if scenarios, and only validated decisions return to the physical network.

What would settle it

Look up the cited primary sources for the survey's headline numbers: if the 99% anomaly-detection accuracy, the 99.997%/99.999% F1-scores, or the 96% FedTwin accuracy cannot be found as reported, or if reference [68]'s actual content does not match either of the two descriptions given in the survey, the survey's reliability as a reference is falsified. A literature search that finds an earlier survey covering the same domain set would falsify the 'first comprehensive survey' claim.

Watch

Extended reading notes

Core claim

The central claim is that the network digital twin is an end-to-end mirror of the entire 6G ecosystem, not a single-domain simulator. The survey's architecture starts with DT-RAN (plus O-RAN and reconfigurable intelligent surfaces), rises through transport, 5GCORE+, cloud/edge, applications, non-terrestrial networks, and quantum networking, and ties them together with continuous two-way synchronization: telemetry flows in, simulations and AI/ML run in the twin, and validated actions flow back to the physical network. The authors distinguish this from traditional simulation, which studies predefined scenarios offline, and they assert that no prior survey covers the full chain with both academic and industrial implementations, with the RAN and 5GCORE+ usage guide being their specific first. A sympathetic reading of the paper is that its contribution is synthesis: it makes the field legible by sorting primary works domain by domain and use case by use case, so the reader can see what has been tried and what remains open.

Load-bearing premise

The survey's value rests on the accuracy and correct attribution of the hundreds of primary studies it summarizes, a premise visibly strained by the same cited work being described as an optical-amplifier modeling method in Table IV and as a blockchain-based twin framework in Section VI-A; it also rests on the claim that no earlier survey covers the same full set of domains.

Editorial extensions

If this is right

  • If the map is accurate, network digital twins move from a niche simulation tool to an organizing framework for 6G, with every layer of the network able to carry its own twin.
  • Operators and vendors get a concrete starting point for NDT in RAN and 5GCORE+, shortening the path from prototype to closed-loop automation.
  • The collected use cases imply that AI/ML models can be trained, tested, and stress-tested in the twin before touching the live network, which is the paper's core argument for the twin as a risk-free testbed.
  • The NTN and quantum sections extend the same data-model-mapping-interface pattern to satellite constellations, UAV swarms, QKD systems, and quantum-classical hybrid networks.
  • The survey's taxonomy provides a shared vocabulary for comparing future NDT work, a precondition for the standardized frameworks the paper names as a future direction.

Reading between the lines

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

  • A practical extension of the survey's four-element definition is a design checklist: any proposed NDT should specify its data sources, model fidelity, synchronization/mapping loop, and interfaces to the physical network.
  • The paper's repeated emphasis on closed-loop validation suggests that the first high-value deployments will be where mistakes are most expensive, such as autonomous vehicle networks, remote surgery, and satellite constellations.
  • If the 'first comprehensive survey' claim holds, the paper is likely to become the baseline that future NDT surveys position themselves against; one check is whether later work adopts its layer taxonomy.
  • A living registry of NDT implementations with reproducible metrics across the surveyed domains would be a natural next step, turning the survey's map into an updatable benchmark.
Share X Bluesky LinkedIn Reddit HN

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. This manuscript is a survey of network digital twin (NDT) techniques across 6G-and-beyond domains, covering radio access networks (RAN) and O-RAN, optical transport networks, 5G core networks and beyond (5GCORE+), cloud/edge computing, vertical applications (blockchain, health, manufacturing, security, vehicular networks), non-terrestrial networks, and quantum networking. It proposes an end-to-end NDT architecture, reviews academic and industrial use cases, provides summary tables for several domains, and closes with challenges and future research directions. The paper's stated contribution is to be the first survey covering this breadth from both academic and industry viewpoints, and in particular the first to provide an in-depth guide to NDT for RAN and 5GCORE+.

Significance. If the survey's attributions are accurate, it would be a useful reference entry point because of its breadth and its inclusion of industry-oriented use cases and standards. The manuscript compiles numerous concrete quantitative results (e.g., 99% anomaly detection accuracy, F1-scores of 99.997% and 99.999%, 96% FedTwin local test accuracy) that could help readers benchmark approaches. The contribution is organizational rather than derivational, so the accuracy of the summarized claims and the correctness of the citation mapping are the main scientific burden. These strengths are currently undermined by visible citation collisions and unfinished editorial passages, which prevent the survey from being relied upon as a trustworthy reference without correction.

major comments (3)
  1. [Section VI-A and Table IV] The same numeric reference keys are assigned to different works in different parts of the manuscript. In Table IV, [68] is described as 'NN-based EDFA mirror modeling' and [69] as 'SNR optimization for the multi-span DT system'; in Section VI-A, [68] is 'FedTwin' (a blockchain-enabled asynchronous federated learning framework) and [69] is a hierarchical FL/blockchain IIoT framework. References [70]–[72] are likewise reused for optical-network optimization in Table IV and for blockchain/FL methods in Section VI-A. Since a single numbered bibliography cannot contain two distinct works under the same key, the quantitative results attributed to these keys, including the 96% FedTwin accuracy in Section VI-A, are not traceable. The bibliography must be corrected so that each key identifies exactly one work.
  2. [Sections II-E and III] Reference [56] is also used for two unrelated purposes: in Section II-E it is cited together with [57] for intelligent reflecting surfaces, while in Section III it is cited as the digital-twin-assisted meta-learning framework for soft-failure localization in ROADMs. This is another reference-number collision that must be resolved before the survey's attributions can be verified.
  3. [Sections IV-A and IV-B] Section IV-A contains an unresolved placeholder in 'a 5, and a public data network (PDN)' and later refers repeatedly to 'the 5' where '5G core network' is clearly intended. Section IV-B repeats the same passage on machine-learning-based anomaly prediction almost verbatim in consecutive paragraphs. These defects do not by themselves invalidate the survey's content, but they indicate that the manuscript has not received the careful checking that a reference survey requires, and they compound the citation-mapping problem.
minor comments (5)
  1. [Section II-C1] There is a typo in 'Futhermore, There are numerous RAN-NDT applications'; 'Futhermore' should be 'Furthermore'.
  2. [Section V, Table V] The table header reads 'SUMMARY OFCLOUD/EDGE COMPUTING APPROACHES'; a space is missing between 'OF' and 'CLOUD'.
  3. [Section VI] The introductory paragraph describes the section as 'various DT for radio access networks in a number of domains', but the section actually covers application-layer digital twins (blockchain, health, manufacturing, security, vehicular networks); the description should be corrected.
  4. [Fig. 1] Figure 1 contains stray text ('ssssss E. What is DT-RAN?' and other fragments) and appears to be a draft outline rather than a clean figure; it should be regenerated.
  5. [Sections IV-C and VIII] There are duplicated phrases such as 'In [79], Sanz et al. Sanz et al.' and 'In [81], In [81], Jiadai et al.'; these should be cleaned up throughout.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a literature survey; its claims are descriptive and its load-bearing content is external attribution, not a derivation from fitted inputs.

full rationale

This manuscript claims no derivational results; it summarizes existing work on network digital twins across RAN, transport, core, cloud/edge, applications, NTN, and quantum networking. There are no equations whose output equals their input, no fitted parameters renamed as predictions, and no uniqueness theorem imported from the authors' prior work. The central novelty claim ('we are the first to provide an in-depth guide and usage of RAN and 5GCORE+ for NDT') is a scope claim about the literature, not a result derived from the cited papers; it can be true or false but is not circular. The visible defects—reference [68] describing an EDFA mirror model in Table IV and FedTwin in Section VI-A, a duplicated passage in Sections II-D2 and IV-B, and the unresolved placeholder 'a 5' in Section IV-A—are accuracy/attribution and editorial-quality problems. They undermine verifiability of the survey's summaries, but they do not make any 'prediction' reduce to an input or make any derivation equivalent to its premise. Under the specified circularity definitions, a survey that derives nothing has no circular step, so the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The survey introduces no free parameters and no invented entities. Its assumptions are the ordinary ones of a review: that the underlying papers are faithfully summarized, that the literature search is complete, and that the organizing concept (NDT) is the right lens. The first two assumptions are partially strained by the visible citation collision and editing defects.

assumptions (3)
  • domain assumption Cited performance numbers are quoted accurately from the source papers.
    The survey repeats metrics such as 96% FL accuracy, 99% anomaly detection, and 99.999% F1-score from cited works without independent verification; the [68] collision in Table IV vs. Section VI-A shows at least one attribution is unreliable.
  • domain assumption The literature search is complete enough to support the 'first comprehensive survey' claim.
    Section I compares only against surveys [6], [9], [10], [11]; no search protocol or inclusion criteria are disclosed in the reviewed portion.
  • domain assumption Digital twin technology is the appropriate organizing lens for 6G network management.
    The survey frames NDT as a solution throughout Sections I and IX without a critical comparison to non-twin alternatives such as direct cloud-native automation.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Network Digital Twin for 6G and Beyond: An End-to-End View Across Multi-Domain Network Ecosystems." pith.science (2026). https://pith.science/paper/S7NREAGA

@misc{pith2026250601609,
  author       = {Pith},
  title        = {Pith review of: Network Digital Twin for 6G and Beyond: An End-to-End View Across Multi-Domain Network Ecosystems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S7NREAGA}},
  note         = {Machine review of arXiv:2506.01609}
}
read the original abstract

With the rapid development of technology, the number of smart mobile users is increasing, accompanied by growing demands from applications such as virtual/augmented reality (VR/XR), remote surgery, autonomous vehicles, and real-time holographic communications, all of which require high transmission rates and ultra-low latency in 6G and beyond networks (6G+). This poses enormous challenges in efficiently deploying large-scale networks, including network design, planning, troubleshooting, optimization, and maintenance, without affecting the user experience. Network Digital Twin (NDT) has emerged as a potential solution, enabling the creation of a virtual model that reflects the actual network, supporting the simulation of various network designs, applying diverse operating policies, and reproducing complex fault scenarios under real-world conditions. This motivate us for this study, where we provide a comprehensive survey of NDT in the context of 6G+, covering areas such as radio access networks (RAN), transport networks, 5G core networks and beyond (5GCORE+), cloud/edge computing, applications (blockchain, health system, manufacturing, security, and vehicular networks), non-terrestrial networks (NTNs), and quantum networks, from both academic and industrial perspectives. In particular, we are the first to provide an in-depth guide and usage of RAN and 5GCORE+ for NDT. Then, we provide an extensive review of foundation technologies such as transport networks, cloud/edge computing, applications, NTNs, and quantum networks in NDT. Finally, we discuss the key challenges, open issues, and future research directions for NDT in the context of 6G+.

Figures

Figures reproduced from arXiv: 2506.01609 by the authors.

Figure 1
Figure 1. The overall structure of this paper. A. What Is DT-RAN? A DT-RAN is a detailed, virtual replica of the corresponding physical RAN and its environment, integrating ray tracing, channel emulation, antenna modeling, mobility patterns, algo￾rithms, and protocols, among others [13]. It is usually built as a layered, software-defined platform that mirrors both the physical RAN infrastructure and its operational context in… view at source ↗
Figure 2
Figure 2. End-to-End Perspective of Network Digital Twin Architecture. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. RAN and CRAN architectures. ments that traditional network management solutions, such as network overprovisioning or admission control, struggle to meet cost-effectively. For instance, advanced communi￾cation technologies like augmented/virtual reality (AR/VR) and holographic/360◦ video streaming demand ultra-low deter￾ministic latency. Meanwhile, modern industrial advancements, such as autonomous vehicles, require … view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: VRAN and O-RAN architectures. With the explosion in the number of mobile users, ef￾fectively allocating resources amidst many complex factors (e.g., massive traffic demand, MU mobility, congestion, en￾vironmental influences) becomes extremely complicated and increasing…
Figure 5
Figure 5. Figure 5: Logical architecture of O-RAN [43]. low latency and high reliability, reported at 99.99%. c. Network Performance Evaluation: Simulations of RAN networks have become powerful tools for evaluating the performance of various RAN products and exploring solutions and method…
Figure 6
Figure 6. Figure 6: Digital twin framework for optical communication system [60]. [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]
Figure 7
Figure 7. Figure 7: 5G system architecture. slicing with different latency and data rate requirement are created on the top of one physical network hardware. More specifically, it manages these different sessions and slicing. Specifically, it can be deployed in a standalone NF or combined…
Figure 8
Figure 8. Figure 8: Cloud-based DT model. [94] digital versions. Furthermore, a DT-based approach addresses the VEC offloading problem, employing vehicle and roadside unit (RSU) digital models. A Deep Reinforcement Learning (DRL)-based offloading scheme is developed to minimize total late…
Figure 9
Figure 9. Figure 9: Edge-based DT framework in cross-company production net [PITH_FULL_IMAGE:figures/full_fig_p020_9.png]
Figure 10
Figure 10. Figure 10: The FL-based framework with user, edge, and DT layers. [PITH_FULL_IMAGE:figures/full_fig_p021_10.png]
Figure 11
Figure 11. Figure 11: DT-based SFL framework with blockchain technology. [PITH_FULL_IMAGE:figures/full_fig_p023_11.png]
Figure 12
Figure 12. Figure 12: The DT-based structural health monitoring framework with structure [PITH_FULL_IMAGE:figures/full_fig_p025_12.png]
Figure 13
Figure 13. Figure 13: The DT-enabled IIoT framework with FL and DRL approaches. [PITH_FULL_IMAGE:figures/full_fig_p026_13.png]
Figure 14
Figure 14. Figure 14: The function classifications of the DT system [138]. [PITH_FULL_IMAGE:figures/full_fig_p028_14.png]
Figure 15
Figure 15. Figure 15: The DT-based framework for intelligent cooperation of UAV swarm. [PITH_FULL_IMAGE:figures/full_fig_p029_15.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

177 extracted references · 72 canonical work pages

  1. [9]

    A survey on digital twins: Architecture, enabling technologies, security and privacy, and future prospects,

    Y . Wang, Z. Su, S. Guo, M. Dai, T. H. Luan, and Y . Liu, “A survey on digital twins: Architecture, enabling technologies, security and privacy, and future prospects,”IEEE Internet of Things Journal, vol. 10, no. 17, pp. 14 965–14 987, 2023

    2023

  2. [6]

    Digital twins: A survey on enabling technologies, challenges, trends and future prospects,

    S. Mihai, M. Yaqoob, D. V . Hung, W. Davis, P. Towakel, M. Raza, M. Karamanoglu, B. Barn, D. Shetve, R. V . Prasadet al., “Digital twins: A survey on enabling technologies, challenges, trends and future prospects,”IEEE Communications Surveys & Tutorials, vol. 24, no. 4, pp. 2255–2291, 2022

    2022

  3. [10]

    Survey on digital twin edge networks (diten) toward 6g,

    F. Tang, X. Chen, T. K. Rodrigues, M. Zhao, and N. Kato, “Survey on digital twin edge networks (diten) toward 6g,”IEEE Open Journal of the Communications Society, vol. 3, pp. 1360–1381, 2022

    2022

  4. [11]

    Empowering 6g communication systems with digital twin technology: A comprehensive survey,

    N. P. Kuruvatti, M. A. Habibi, S. Partani, B. Han, A. Fellan, and H. D. Schotten, “Empowering 6g communication systems with digital twin technology: A comprehensive survey,”IEEE access, vol. 10, pp. 112 158–112 186, 2022

    2022

  5. [68]

    Fedtwin: Blockchain- enabled adaptive asynchronous federated learning for digital twin networks,

    Y . Qu, L. Gao, Y . Xiang, S. Shen, and S. Yu, “Fedtwin: Blockchain- enabled adaptive asynchronous federated learning for digital twin networks,”IEEE Network, vol. 36, no. 6, pp. 183–190, 2022

    2022

  6. [69]

    Reinforcing industry 4.0 with digital twins and blockchain-assisted federated learning,

    M. Aloqaily, I. A. Ridhawi, and S. Kanhere, “Reinforcing industry 4.0 with digital twins and blockchain-assisted federated learning,”IEEE Journal on Selected Areas in Communications, vol. 41, no. 11, pp. 3504–3516, 2023

    2023

  7. [52]

    Federated Learning for Anomaly Detection in Open RAN: Security Architecture Within a Digital Twin,

    Y . Rumesh, D. Attanayaka, P. Porambage, J. Pinola, J. Groen, and K. Chowdhury, “Federated Learning for Anomaly Detection in Open RAN: Security Architecture Within a Digital Twin,” 2024

    2024

  8. [80]

    Deep Learning-Based Modeling of 5G Core Control Plane for 5G Network Digital Twin,

    Z. Tao, Y . Guo, G. He, Y . Huang, and X. You, “Deep Learning-Based Modeling of 5G Core Control Plane for 5G Network Digital Twin,” IEEE Transactions on Cognitive Communications and Networking, vol. 10, no. 1, pp. 238–251, 2024

    2024

  9. [70]

    Blockchain-based decentralized learning for security in digital twins,

    Z. Lv, C. Cheng, and H. Lv, “Blockchain-based decentralized learning for security in digital twins,”IEEE Internet of Things Journal, vol. 10, no. 24, pp. 21 479–21 488, 2023

    2023

  10. [72]

    Digital twins-enabled federated learning in mobile networks: From the per- spective of communication-assisted sensing,

    J. Mu, W. Ouyang, T. Hong, W. Yuan, Y . Cui, and Z. Jing, “Digital twins-enabled federated learning in mobile networks: From the per- spective of communication-assisted sensing,”IEEE Journal on Selected Areas in Communications, vol. 41, no. 10, pp. 3230–3241, 2023

    2023

  11. [56]

    Performance Analysis of User Pairing for Active RIS-Enabled Cooperative NOMA in 6G Cognitive Radio Networks,

    P. T. Tin, M.-S. V . Nguyen, D.-H. Tran, C. T. Nguyen, S. Chatzinotas, Z. Ding, and M. V oznak, “Performance Analysis of User Pairing for Active RIS-Enabled Cooperative NOMA in 6G Cognitive Radio Networks,”IEEE Internet of Things Journal, vol. 11, no. 23, pp. 37 675–37 692, 2024

    2024

  12. [57]

    Deep Learning-Driven Throughput Maximization in Covert Communication for UA V-RIS Cognitive Sys- tems,

    N. Q. Long, V .-H. Dang, T. D. Ho, H. Tran, S. Chatzinotas, D.-H. Tran, S. Sanguanpong, C. So-Inet al., “Deep Learning-Driven Throughput Maximization in Covert Communication for UA V-RIS Cognitive Sys- tems,”IEEE Open Journal of the Communications Society, 2025

    2025

Show all 177 references
  1. [1]

    The road towards 6g: A comprehensive survey,

    W. Jiang, B. Han, M. A. Habibi, and H. D. Schotten, “The road towards 6g: A comprehensive survey,”IEEE Open Journal of the Communications Society, vol. 2, pp. 334–366, 2021

    2021

  2. [2]

    6g internet of things: A comprehensive survey,

    D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, D. Niyato, O. Dobre, and H. V . Poor, “6g internet of things: A comprehensive survey,”IEEE Internet of Things Journal, vol. 9, no. 1, pp. 359–383, 2021

    2021

  3. [3]

    Digital- twin-enabled 6g: Vision, architectural trends, and future directions,

    L. U. Khan, W. Saad, D. Niyato, Z. Han, and C. S. Hong, “Digital- twin-enabled 6g: Vision, architectural trends, and future directions,” IEEE Communications Magazine, vol. 60, no. 1, pp. 74–80, 2022

    2022

  4. [4]

    Wireless Network Digital Twin for 6G: Generative AI as a Key Enabler,

    Z. Tao, W. Xu, Y . Huang, X. Wang, and X. You, “Wireless Network Digital Twin for 6G: Generative AI as a Key Enabler,”IEEE Wireless Communications, vol. 31, no. 4, pp. 24–31, 2024

    2024

  5. [5]

    Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems,

    M. Grieves and J. Vickers, “Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems,”Transdisciplinary perspectives on complex systems: New findings and approaches, pp. 85–113, 2017

    2017

  6. [7]

    What is a digital twin?

    IBM, “What is a digital twin?” Jan 2025. [Online]. Available: https://www.ibm.com/think/topics/what-is-a-digital-twin

    2025

  7. [8]

    Digital twin for enhanced resource allocation in 6g non-terrestrial networks,

    H. Al-Hraishawi, M. Alsenwi, J. Ur Rehman, E. Lagunas, and S. Chatzinotas, “Digital twin for enhanced resource allocation in 6g non-terrestrial networks,”IEEE Communications Magazine, vol. 63, no. 3, pp. 47–53, 2025

    2025

  8. [12]

    6G digital twin networks: From theory to practice,

    X. Lin, L. Kundu, C. Dick, E. Obiodu, T. Mostak, and M. Flaxman, “6G digital twin networks: From theory to practice,”IEEE Communications Magazine, vol. 61, no. 11, pp. 72–78, 2023

    2023

  9. [13]

    Future technology trends of terrestrial IMT systems towards 2030 and beyond,

    ITU-R, “Future technology trends of terrestrial IMT systems towards 2030 and beyond,”Report ITU-R M.2516-0, 2022

    2022

  10. [14]

    Digital twin network–requirements and architecture,

    ITU-T, “Digital twin network–requirements and architecture,”Recom- mendation ITU-T Y.3090, 2022

    2022

  11. [15]

    Digital twin RAN: Key enablers,

    O-RAN nGRG, “Digital twin RAN: Key enablers,”Report ID: RR- 2024-09, 2024

    2024

  12. [16]

    Learning radio environments by differentiable ray tracing,

    J. Hoydis, F. Aït Aoudia, S. Cammerer, F. Euchner, M. Nimier- David, S. Ten Brink, and A. Keller, “Learning radio environments by differentiable ray tracing,”IEEE Transactions on Machine Learning in Communications and Networking, vol. 2, pp. 1527–1539, 2024

    2024

  13. [17]

    Calibrating wireless ray tracing for digital twinning using local phase error es- timates,

    C. Ruah, O. Simeone, J. Hoydis, and B. Al-Hashimi, “Calibrating wireless ray tracing for digital twinning using local phase error es- timates,”IEEE Transactions on Machine Learning in Communications and Networking, vol. 2, pp. 1193–1215, 2024

    2024

  14. [18]

    End-to-end network SLA quality assurance for C-RAN: A closed-loop management method based on digital twin network,

    Y . Ren, S. Guo, B. Cao, and X. Qiu, “End-to-end network SLA quality assurance for C-RAN: A closed-loop management method based on digital twin network,”IEEE Transactions on Mobile Computing, vol. 23, no. 5, pp. 4405–4422, 2023

    2023

  15. [19]

    Study on management aspect of network digital twin,

    3GPP, “Study on management aspect of network digital twin,”TR 28.915, 2025

    2025

  16. [20]

    Management and orchestration; management aspects of network digital twins,

    ——, “Management and orchestration; management aspects of network digital twins,”TS 28.561, 2025

    2025

  17. [21]

    Advancing next generation wireless networks with digital twin: Construction, valida- tion, and real-world applications on an indoor over-the-air testbed,

    B. Akgun, A. Jolly, B. Sachdev, D. Ravichandran, R. Amiri, V . Jain, M. Jayabalan, Y . Chen, H. Pathak, V . Chandeet al., “Advancing next generation wireless networks with digital twin: Construction, valida- tion, and real-world applications on an indoor over-the-air testbed,”...

    2024

  18. [22]

    Digital twin approach to estimating and utilizing the capacity region of wireless ad hoc networks,

    Y . Thomas, S. Toumpis, and N. Smyrnioudis, “Digital twin approach to estimating and utilizing the capacity region of wireless ad hoc networks,”Computer Networks, vol. 241, p. 110213, 2024

    2024

  19. [23]

    Digital twin enabled 6G radio testing: Concepts, challenges and solutions,

    H. Gao, P. Kyösti, X. Zhang, and W. Fan, “Digital twin enabled 6G radio testing: Concepts, challenges and solutions,”IEEE Communica- tions Magazine, vol. 61, no. 11, pp. 88–94, 2023

    2023

  20. [24]

    Design of a standard-compliant real-time neural receiver for 5G NR,

    R. Wiesmayr, S. Cammerer, F. A. Aoudia, J. Hoydis, J. Zakrzewski, and A. Keller, “Design of a standard-compliant real-time neural receiver for 5G NR,” inIEEE International Conference on Machine Learning for Communication and Networking, 2025, pp. 1–6. 37

    2025

  21. [25]

    Multiverse at the edge: Interacting real world and digital twins for wireless beamforming,

    B. Salehi, U. Demir, D. Roy, S. Pradhan, J. Dy, S. Ioannidis, and K. Chowdhury, “Multiverse at the edge: Interacting real world and digital twins for wireless beamforming,”IEEE/ACM Transactions on Networking, vol. 32, no. 4, pp. 3092–3110, 2024

    2024

  22. [26]

    Research report on digital twin RAN use cases,

    O-RAN nGRG, “Research report on digital twin RAN use cases,” Report ID: RR-2024-07, 2024

    2024

  23. [27]

    Toward intelligent cyber-physical systems: Digital twin meets artificial intelligence,

    M. Groshev, C. Guimaraes, J. Martín-Pérez, and A. de la Oliva, “Toward intelligent cyber-physical systems: Digital twin meets artificial intelligence,”IEEE Communications Magazine, vol. 59, no. 8, pp. 14– 20, 2021

    2021

  24. [28]

    Ransight: Pro- grammable telemetry for next-generation open radio access networks,

    A. Kak, V .-Q. Pham, H.-T. Thieu, and N. Choi, “Ransight: Pro- grammable telemetry for next-generation open radio access networks,” inGLOBECOM 2023 - 2023 IEEE Global Communications Confer- ence, 2023, pp. 5391–5396

    2023

  25. [29]

    Digital twins of electromagnetic propagation environments for live 5G networks– Part I: Channel acquisition, EM simulation and verification,

    L. Li, H. Sun, Z. Wang, W. Wang, and W. Fan, “Digital twins of electromagnetic propagation environments for live 5G networks– Part I: Channel acquisition, EM simulation and verification,”IEEE Transactions on Antennas and Propagation, vol. 73, no. 4, pp. 2053– 2064, 2025

    2025

  26. [30]

    Digital twins of electromagnetic propagation environments for live 5G networks—Part II: High-fidelity emulation in the MPAC setup,

    L. Li, S. Lin, Y . Zhong, Z. Wang, and W. Wang, “Digital twins of electromagnetic propagation environments for live 5G networks—Part II: High-fidelity emulation in the MPAC setup,”IEEE Transactions on Antennas and Propagation, vol. 73, no. 4, pp. 2065–2073, 2025

    2025

  27. [31]

    Toward digital network twins: Integrating Sionna RT in NS3 for 6G multi-RAT networks simulations,

    R. Pegurri, F. Linsalata, E. Moro, J. Hoydis, and U. Spagnolini, “Toward digital network twins: Integrating Sionna RT in NS3 for 6G multi-RAT networks simulations,”arXiv preprint arXiv:2501.00372, 2024

    2024

  28. [32]

    Digital twin for 5G and beyond,

    H. X. Nguyen, R. Trestian, D. To, and M. Tatipamula, “Digital twin for 5G and beyond,”IEEE Communications Magazine, vol. 59, no. 2, pp. 10–15, 2021

    2021

  29. [33]

    5G air-to- ground network design and optimization: A deep learning approach,

    Y . Chen, X. Lin, T. Khan, M. Afshang, and M. Mozaffari, “5G air-to- ground network design and optimization: A deep learning approach,” in IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), 2021, pp. 1–6

    2021

  30. [34]

    GNN-based power allocation and user association in digital twin network for the terahertz band,

    H. Zhang, X. Ma, X. Liu, L. Li, and K. Sun, “GNN-based power allocation and user association in digital twin network for the terahertz band,”IEEE Journal on Selected Areas in Communications, vol. 41, no. 10, pp. 3111–3121, 2023

    2023

  31. [35]

    Coverage optimization for large-scale mobile networks with digital twin and multi-agent rein- forcement learning,

    H. Liu, T. Li, F. Jiang, W. Su, and Z. Wang, “Coverage optimization for large-scale mobile networks with digital twin and multi-agent rein- forcement learning,”IEEE Transactions on Wireless Communications, vol. 23, no. 12, pp. 18 316–18 330, 2024

    2024

  32. [36]

    When digital twin meets generative AI: Intelligent closed-loop network man- agement,

    X. Huang, H. Yang, C. Zhou, M. He, X. Shen, and W. Zhuang, “When digital twin meets generative AI: Intelligent closed-loop network man- agement,”IEEE Network, pp. 1–1, 2024

    2024

  33. [37]

    Colosseum as a digital twin: Bridging real-world experimentation and wireless network emulation,

    D. Villa, M. Tehrani-Moayyed, C. P. Robinson, L. Bonati, P. Johari, M. Polese, and T. Melodia, “Colosseum as a digital twin: Bridging real-world experimentation and wireless network emulation,”IEEE Transactions on Mobile Computing, vol. 23, no. 10, pp. 9150–9166, 2024

    2024

  34. [38]

    Distributed digital twin migration in multi-tier computing systems,

    Z. Chen, W. Yi, A. Nallanathan, and J. A. Chambers, “Distributed digital twin migration in multi-tier computing systems,”IEEE Journal of Selected Topics in Signal Processing, vol. 18, no. 1, pp. 109–123, 2024

    2024

  35. [39]

    UA V Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization,

    D.-H. Tran, V .-D. Nguyen, S. Chatzinotas, T. X. Vu, and B. Ottersten, “UA V Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization,”IEEE Transactions on Wireless Communications, vol. 21, no. 3, pp. 1621–1637, 2022

    2022

  36. [40]

    Backscatter-Assisted Data Of- floading in OFDMA-Based Wireless-Powered Mobile Edge Computing for IoT Networks,

    P. X. Nguyen, D.-H. Tran, O. Onireti, P. T. Tin, S. Q. Nguyen, S. Chatzinotas, and H. Vincent Poor, “Backscatter-Assisted Data Of- floading in OFDMA-Based Wireless-Powered Mobile Edge Computing for IoT Networks,”IEEE Internet of Things Journal, vol. 8, no. 11, pp. 9233–9243, 2021

    2021

  37. [41]

    Coarse Trajectory Design for Energy Minimization in UA V- Enabled,

    D.-H. Tran, T. X. Vu, S. Chatzinotas, S. ShahbazPanahi, and B. Ot- tersten, “Coarse Trajectory Design for Energy Minimization in UA V- Enabled,”IEEE Transactions on Vehicular Technology, vol. 69, no. 9, pp. 9483–9496, 2020

    2020

  38. [42]

    O-RAN Empowering Vertical Industry: Scenarios, Solu- tions and Best Practice,

    O. Alliance, “O-RAN Empowering Vertical Industry: Scenarios, Solu- tions and Best Practice,”White Paper, Dec., 2023

    2023

  39. [43]

    O-RAN Architecture Description 11.0,

    ——, “O-RAN Architecture Description 11.0,”White Paper, Feb., 2024

    2024

  40. [44]

    O-RAN.WG1.Slicing-Architecture-R003-v09.00,

    ——, “O-RAN.WG1.Slicing-Architecture-R003-v09.00,”Specifica- tion, March, 2023

    2023

  41. [45]

    Network digital twins - outlook and opportunities,

    P. Ohlen, C. Johnston, H. Olofsson, S. Terrill, and F. Chernogorov, “Network digital twins - outlook and opportunities,”Ericsson Technol- ogy Review, vol. 2022, no. 12, pp. 2–11, 2022

    2022

  42. [46]

    Network Digital Twin: Context, Enabling Technologies, and Opportunities,

    P. Almasan, M. Ferriol-Galmes, J. Paillisse, J. Suarez-Varela, D. Perino, D. Lopez, A. A. P. Perales, P. Harvey, L. Ciavaglia, L. Wong, V . Ram, S. Xiao, X. Shi, X. Cheng, A. Cabellos-Aparicio, and P. Barlet- Ros, “Network Digital Twin: Context, Enabling Technologies, and Oppo...

    2022

  43. [47]

    Network Digital Twin: Concepts and Reference Architecture,

    China Mobile, Telefonica, Huawei, Orange, “Network Digital Twin: Concepts and Reference Architecture,”Specification, March, 2024

    2024

  44. [48]

    Network Digital Twin for Open RAN: The Key Enablers, Standardization, and Use Cases,

    J. Mirzaei, I. Abualhaol, and G. Poitau, “Network Digital Twin for Open RAN: The Key Enablers, Standardization, and Use Cases,”arXiv preprint arXiv:2308.02644, 2023

    2023 arXiv

  45. [49]

    Hexric: Building a better near-real time network controller for the open ran ecosystem,

    V .-Q. Pham, H.-T. Thieu, A. Kak, and N. Choi, “Hexric: Building a better near-real time network controller for the open ran ecosystem,” in Proceedings of the 24th International Workshop on Mobile Computing Systems and Applications, ser. HotMobile ’23. New York, NY , USA: Asso...

    2023

  46. [50]

    Secure Data Dissemination Scheme for Digital Twin Empowered Vehicular Networks in Open RAN,

    R. Kumar, P. Kumar, A. Aljuhani, A. Jolfaei, A. N. Islam, and N. Mohammad, “Secure Data Dissemination Scheme for Digital Twin Empowered Vehicular Networks in Open RAN,”IEEE Transactions on Vehicular Technology, pp. 1–13, 2023

    2023

  47. [51]

    Digital Twin-Driven Trust Management in Open RAN-Based Spatial Crowdsourcing Drone Services,

    J. Akram, A. Anaissi, R. S. Rathore, R. H. Jhaveri, and A. Akram, “Digital Twin-Driven Trust Management in Open RAN-Based Spatial Crowdsourcing Drone Services,”IEEE Transactions on Green Com- munications and Networking, pp. 1–1, 2024

    2024

  48. [53]

    Demo: A Digital Twin of the 5G Radio Access Network for Anomaly Detection Functionality,

    P. Li, A. Aijaz, T. Farnham, S. Gufran, and S. Chintalapati, “Demo: A Digital Twin of the 5G Radio Access Network for Anomaly Detection Functionality,” in2023 IEEE 31st International Conference on Network Protocols (ICNP), 2023, pp. 1–2

    2023

  49. [54]

    Colos- seum: The Open RAN Digital Twin,

    M. Polese, L. Bonati, S. D’Oro, P. Johari, D. Villa, S. Velumani, R. Gangula, M. Tsampazi, C. P. Robinson, G. Gemmiet al., “Colos- seum: The Open RAN Digital Twin,”arXiv preprint arXiv:2404.17317, 2024

    2024 arXiv

  50. [55]

    Digital Twin Assisted Closed-Loops for Energy-Efficient Open RAN-Based Fixed Wireless Access Provisioning in Rural Areas,

    A. Ndikumana, K. K. Nguyen, and M. Cheriet, “Digital Twin Assisted Closed-Loops for Energy-Efficient Open RAN-Based Fixed Wireless Access Provisioning in Rural Areas,” inGLOBECOM 2023 - 2023 IEEE Global Communications Conference, 2023, pp. 6285–6290

    2023

  51. [58]

    Towards efficient reflectarray digital twins - an em-driven machine learning perspective,

    G. Oliveri, M. Salucci, and A. Massa, “Towards efficient reflectarray digital twins - an em-driven machine learning perspective,”IEEE Transactions on Antennas and Propagation, vol. 70, no. 7, pp. 5078– 5093, 2022

    2022

  52. [59]

    Digital twin-aided learning for managing reconfigurable intelligent surface-assisted, uplink, user- centric cell-free systems,

    Y . Cui, T. Lv, W. Ni, and A. Jamalipour, “Digital twin-aided learning for managing reconfigurable intelligent surface-assisted, uplink, user- centric cell-free systems,”IEEE Journal on Selected Areas in Commu- nications, vol. 41, no. 10, pp. 3175–3190, 2023

    2023

  53. [60]

    The role of digital twin in optical communication: Fault management, hardware configuration, and transmission simulation,

    D. Wang, Z. Zhang, M. Zhang, M. Fu, J. Li, S. Cai, C. Zhang, and X. Chen, “The role of digital twin in optical communication: Fault management, hardware configuration, and transmission simulation,” IEEE Communications Magazine, vol. 59, no. 1, pp. 133–139, 2021

    2021

  54. [61]

    Ocata: a deep-learning-based digital twin for the optical time domain,

    D. Sequeira, M. Ruiz, N. Costa, A. Napoli, J. Pedro, and L. Velasco, “Ocata: a deep-learning-based digital twin for the optical time domain,” Journal of Optical Communications and Networking, vol. 15, no. 2, pp. 87–97, 2023

    2023

  55. [62]

    Applications of the ocata time domain digital twin: from qot estimation to failure management,

    M. Devigili, M. Ruiz, N. Costa, C. Castro, A. Napoli, J. Pedro, and L. Velasco, “Applications of the ocata time domain digital twin: from qot estimation to failure management,”Journal of Optical Communi- cations and Networking, vol. 16, no. 2, pp. 221–232, 2024

    2024

  56. [63]

    Applying digital twins to optical networks with cloud-native sdn controllers,

    R. Vilalta, L. Gifre, R. Casellas, R. Muñoz, R. Martínez, A. Mozo, A. Pastor, D. López, and J. P. Fernández-Palacios, “Applying digital twins to optical networks with cloud-native sdn controllers,”IEEE Communications Magazine, vol. 61, no. 12, pp. 128–134, 2023

    2023

  57. [64]

    Building a digital twin for large-scale and dy- namic c+l-band optical networks,

    Y . Zhang, M. Zhang, Y . Song, Y . Shi, C. Zhang, C. Ju, B. Guo, S. Huang, and D. Wang, “Building a digital twin for large-scale and dy- namic c+l-band optical networks,”Journal of Optical Communications and Networking, vol. 15, no. 12, pp. 985–998, 2023. 38

    2023

  58. [65]

    Building a digital twin of an edfa for optical networks: a gray-box modeling approach,

    Y . Liu, X. Liu, Y . Zhang, M. Cai, M. Fu, X. Zhong, L. Yi, W. Hu, and Q. Zhuge, “Building a digital twin of an edfa for optical networks: a gray-box modeling approach,”Journal of Optical Communications and Networking, vol. 15, no. 11, pp. 830–838, 2023

    2023

  59. [66]

    Digital twin modeling and controlling of optical power evolution enabling autonomous-driving optical networks: a Bayesian approach,

    X. Liu, Y . Zhang, Y . Chen, Y . Liu, M. Cai, Q. Qiu, M. Fu, L. Yi, W. Hu, and Q. Zhuge, “Digital twin modeling and controlling of optical power evolution enabling autonomous-driving optical networks: a Bayesian approach,”Advanced Photonics, vol. 6, no. 2, p. 026006,

  60. [67]

    Digital-twin-assisted meta learning for soft-failure localization in roadm-based optical networks,

    R. Wang, J. Zhang, Z. Gu, M. Ibrahimi, B. Zhang, F. Musumeci, M. Tornatore, and Y . Ji, “Digital-twin-assisted meta learning for soft-failure localization in roadm-based optical networks,”Journal of Optical Communications and Networking, vol. 16, no. 7, pp. C11–C19, 2024

    2024

  61. [71]

    Fortifying federated learning in iiot: Leveraging blockchain and digital twin innovations for enhanced security and resilience,

    S. B. Prathiba, Y . Govindarajan, V . P. A. Ganesan, A. Ramachandran, A. K. Selvaraj, A. K. Bashir, and T. R. Gadekallu, “Fortifying federated learning in iiot: Leveraging blockchain and digital twin innovations for enhanced security and resilience,”IEEE Access, pp. 1–1, 2024

    2024

  62. [73]

    Multi-Agent Deep Reinforcement Learning for Resilience Optimization in 5G RAN,

    S. Kaada, D.-H. Tran, N. Van Huynh, M.-L. A. Morel, S. Jelassi, and G. Rubino, “Multi-Agent Deep Reinforcement Learning for Resilience Optimization in 5G RAN,”arXiv preprint arXiv:2407.18066, 2024

    2024 arXiv

  63. [74]

    5G System Principles,

    Nokia, “5G System Principles,”IEEE Courses, 2019

    2019

  64. [75]

    The 5G Core Network Demystified,

    G. Gaurav and G. Kevin, “The 5G Core Network Demystified,” https://infohub.delltechnologies.com/en-us/p/the-5g-core-network- demystified/, 2023

    2023

  65. [76]

    System Architecture for the 5G System (3GPP TS 23.501 version 15.3.0 Release 15),

    ETSI, “System Architecture for the 5G System (3GPP TS 23.501 version 15.3.0 Release 15),” https://www.etsi.org/deliver/etsi_ts/123500_123599/123501/15.03.00 _60/ts_123501v150300p.pdf, 2023

    2023

  66. [77]

    Research and Application of Configuration System in Core Network Based on Digital Twin,

    Y . Zheng, C. Lin, S. Huang, W. Li, K. Wang, and J. Lou, “Research and Application of Configuration System in Core Network Based on Digital Twin,” in2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IM- CEC), vol. 5. IEE...

    2022

  67. [78]

    Network Slicing for Zero-Touch Networks: A Top-Notch Technology,

    S. Rani, H. Babbar, M. Krichen, K. Yu, and F. H. Memon, “Network Slicing for Zero-Touch Networks: A Top-Notch Technology,”IEEE Network, vol. 37, no. 5, pp. 16–24, 2023

    2023

  68. [79]

    Digital Twins for 5G Networks: A Modeling and Deployment Methodology,

    M. Sanz Rodrigo, D. Rivera, J. I. Moreno, M. Alvarez-Campana, and D. R. Lopez, “Digital Twins for 5G Networks: A Modeling and Deployment Methodology,”IEEE Access, vol. 11, pp. 38 112–38 126, 2023

    2023

  69. [81]

    Digital Twin-Assisted Flexible Slice Admission Control for 5G Core Network: A Deep Reinforcement Learning Approach,

    J. Wang, J. Li, and J. Liu, “Digital Twin-Assisted Flexible Slice Admission Control for 5G Core Network: A Deep Reinforcement Learning Approach,”Future Generation Computer Systems, vol. 153, pp. 467–476, 2024

    2024

  70. [82]

    MVNOCoreSim: A Digital Twin for Virtualized IoT-Centric Mobile Core Networks,

    S. GeiBler, F. Wamser, W. Bauer, S. Gebert, S. Kounev, and T. HoBfeld, “MVNOCoreSim: A Digital Twin for Virtualized IoT-Centric Mobile Core Networks,”IEEE Internet of Things Journal, vol. 10, no. 15, pp. 13 974–13 987, 2023

    2023

  71. [83]

    A Proof of Concept on Digital Twin-Controlled WiFi Core Network Selection for In-Flight Connectivity,

    T. Bilen, E. Ak, B. Bal, and B. Canberk, “A Proof of Concept on Digital Twin-Controlled WiFi Core Network Selection for In-Flight Connectivity,”IEEE Communications Standards Magazine, vol. 6, no. 3, pp. 60–68, 2022

    2022

  72. [84]

    Digital Twin-Enabled Intelligent DDoS Detection Mechanism for Autonomous Core Networks,

    Y . Yigit, B. Bal, A. Karameseoglu, T. Q. Duong, and B. Canberk, “Digital Twin-Enabled Intelligent DDoS Detection Mechanism for Autonomous Core Networks,”IEEE Communications Standards Mag- azine, vol. 6, no. 3, pp. 38–44, 2022

    2022

  73. [85]

    Leveraging digital twins for healthcare systems engineering,

    N. Mohamed, J. Al-Jaroodi, I. Jawhar, and N. Kesserwan, “Leveraging digital twins for healthcare systems engineering,”Ieee Access, 2023

    2023

  74. [86]

    A novel cloud-based framework for the elderly healthcare services using digital twin,

    Y . Liu, L. Zhang, Y . Yang, L. Zhou, L. Ren, F. Wang, R. Liu, Z. Pang, and M. J. Deen, “A novel cloud-based framework for the elderly healthcare services using digital twin,”IEEE access, vol. 7, pp. 49 088– 49 101, 2019

    2019

  75. [87]

    Towards private similarity query based healthcare monitoring over digital twin cloud platform,

    Y . Zheng, R. Lu, Y . Guan, S. Zhang, and J. Shao, “Towards private similarity query based healthcare monitoring over digital twin cloud platform,” in2021 IEEE/ACM 29th International Symposium on Qual- ity of Service (IWQOS). IEEE, 2021, pp. 1–10

    2021

  76. [88]

    Cloud-edge hosted digital twins for coordinated control of distributed energy resources,

    J. Han, Q. Hong, M. H. Syed, M. A. U. Khan, G. Yang, G. Burt, and C. Booth, “Cloud-edge hosted digital twins for coordinated control of distributed energy resources,”IEEE Transactions on Cloud Computing, vol. 11, no. 2, pp. 1242–1256, 2022

    2022

  77. [89]

    Practical adoption of cloud computing in power systems—drivers, challenges, guidance, and real-world use cases,

    S. Zhang, A. Pandey, X. Luo, M. Powell, R. Banerji, L. Fan, A. Parchure, and E. Luzcando, “Practical adoption of cloud computing in power systems—drivers, challenges, guidance, and real-world use cases,”IEEE Transactions on Smart Grid, vol. 13, no. 3, pp. 2390– 2411, 2022

    2022

  78. [90]

    Digital twin intelligent system for industrial iot-based big data management and analysis in cloud,

    C. L. Stergiou and K. E. Psannis, “Digital twin intelligent system for industrial iot-based big data management and analysis in cloud,”Virtual Reality & Intelligent Hardware, vol. 4, no. 4, pp. 279–291, 2022

    2022

  79. [91]

    A new approach to build industrial internet of things (iiot) systems based on digital twin’s technologies,

    A. Mikhailov, S. Tretyakov, and Y . Andreev, “A new approach to build industrial internet of things (iiot) systems based on digital twin’s technologies,” in2022 International conference on industrial engineering, applications and manufacturing (ICIEAM). IEEE, 2022, pp. 1091–1095

    2022

  80. [92]

    Digital twin-driven online anomaly detection for an automation system based on edge intelligence,

    H. Huang, L. Yang, Y . Wang, X. Xu, and Y . Lu, “Digital twin-driven online anomaly detection for an automation system based on edge intelligence,”Journal of Manufacturing Systems, vol. 59, pp. 138–150, 2021

    2021

  81. [93]

    Digital twin based real-time production logistics synchronization system in a multi-level computing architecture,

    Y . Pan, T. Qu, N. Wu, M. Khalgui, and G. Huang, “Digital twin based real-time production logistics synchronization system in a multi-level computing architecture,”Journal of Manufacturing Systems, vol. 58, pp. 246–260, 2021

    2021

  82. [94]

    Cloud-based digital twinning for structural health monitoring using deep learning,

    H. V . Dang, M. Tatipamula, and H. X. Nguyen, “Cloud-based digital twinning for structural health monitoring using deep learning,”IEEE Transactions on Industrial Informatics, vol. 18, no. 6, pp. 3820–3830, 2021

    2021

  83. [95]

    Adaptive digital twin for vehicular edge computing and networks,

    Y . Dai and Y . Zhang, “Adaptive digital twin for vehicular edge computing and networks,”Journal of Communications and Information Networks, vol. 7, no. 1, pp. 48–59, 2022

    2022

  84. [96]

    Digital twin-enabled 6g aerial edge computing with ultra-reliable and low-latency communications : (invited paper),

    T. Q. Duong, D. Van Huynh, Y . Li, E. Garcia-Palacios, and K. Sun, “Digital twin-enabled 6g aerial edge computing with ultra-reliable and low-latency communications : (invited paper),” in2022 1st Interna- tional Conference on 6G Networking (6GNet), 2022, pp. 1–5

    2022

  85. [97]

    Ecoedgetwin: Enhanced 6g network via mobile edge computing and digital twin integration,

    S. H. Karobi, S. Ahmed, S. R. Sabuj, and A. Khokhar, “Ecoedgetwin: Enhanced 6g network via mobile edge computing and digital twin integration,”arXiv preprint arXiv:2405.06507, 2024

    2024 arXiv

  86. [98]

    Adaptive edge association for wireless digital twin networks in 6g,

    Y . Lu, S. Maharjan, and Y . Zhang, “Adaptive edge association for wireless digital twin networks in 6g,”IEEE Internet of Things Journal, vol. 8, no. 22, pp. 16 219–16 230, 2021

    2021

  87. [99]

    Edge-coordinated energy- efficient video analytics for digital twin in 6g,

    P. Yang, J. Hou, L. Yu, W. Chen, and Y . Wu, “Edge-coordinated energy- efficient video analytics for digital twin in 6g,”China Communications, vol. 20, no. 2, pp. 14–25, 2023

    2023

  88. [100]

    Human activity recognition in mobile edge computing: A low-cost and high-fidelity digital twin approach with deep reinforcement learning,

    C. Wang, Z. Cai, and Y . Li, “Human activity recognition in mobile edge computing: A low-cost and high-fidelity digital twin approach with deep reinforcement learning,”IEEE Transactions on Consumer Electronics, 2024

    2024

  89. [101]

    Realizing immersive communications in human digital twin by edge computing empowered tactile internet: Visions and case study,

    H. Xiang, C. Yi, K. Wu, J. Chen, J. Cai, D. Niyato, and X. Shen, “Realizing immersive communications in human digital twin by edge computing empowered tactile internet: Visions and case study,”IEEE Communications Magazine, 2023

    2023

  90. [102]

    Cardio twin: A digital twin of the human heart running on the edge,

    R. Martinez-Velazquez, R. Gamez, and A. El Saddik, “Cardio twin: A digital twin of the human heart running on the edge,” in2019 IEEE international symposium on medical measurements and applications (MeMeA). IEEE, 2019, pp. 1–6

    2019

  91. [103]

    A distributed digital twin architecture for shop floor monitoring based on edge-cloud collaboration,

    N. Ouahabi, A. Chebak, M. Zegrari, O. Kamach, and M. Berquedich, “A distributed digital twin architecture for shop floor monitoring based on edge-cloud collaboration,” in2021 Third International Conference on Transportation and Smart Technologies (TST). IEEE, 2021, pp. 72–78

    2021

  92. [104]

    Edge-based digital twin to trace and ensure sustainability in cross-company production networks,

    M. Glatt, P. Kölsch, C. Siedler, P. Langlotz, S. Ehmsen, and J. C. Aurich, “Edge-based digital twin to trace and ensure sustainability in cross-company production networks,”Procedia CIRP, vol. 98, pp. 276– 281, 2021

    2021

  93. [105]

    An intelligent edge-based digital twin for robotics,

    L. Girletti, M. Groshev, C. Guimarães, C. J. Bernardos, and A. de la Oliva, “An intelligent edge-based digital twin for robotics,” in2020 IEEE Globecom Workshops (GC Wkshps. IEEE, 2020, pp. 1–6. 39

    2020

  94. [106]

    Edge intelligence-based ultra-reliable and low-latency communications for digital twin-enabled metaverse,

    D. Van Huynh, S. R. Khosravirad, A. Masaracchia, O. A. Dobre, and T. Q. Duong, “Edge intelligence-based ultra-reliable and low-latency communications for digital twin-enabled metaverse,”IEEE Wireless Communications Letters, vol. 11, no. 8, pp. 1733–1737, 2022

    2022

  95. [107]

    Deep Learn- ing for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin,

    R. Dong, C. She, W. Hardjawana, Y . Li, and B. Vucetic, “Deep Learn- ing for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin,”IEEE Transactions on Wireless Communications, vol. 18, no. 10, pp. 4692–4707, 2019

    2019

  96. [108]

    Edge-fog-cloud secure storage with deep-learning- assisted digital twins,

    Z. Lv and R. Lou, “Edge-fog-cloud secure storage with deep-learning- assisted digital twins,”IEEE Internet of Things Magazine, vol. 5, no. 2, pp. 36–40, 2022

    2022

  97. [109]

    Building a Smart Campus Digital Twin: System, Analytics, and Lessons Learned From a Real-World Project,

    L. Roda-Sanchez, F. Cirillo, G. Solmaz, T. Jacobs, C. Garrido-Hidalgo, T. Olivares, and E. Kovacs, “Building a Smart Campus Digital Twin: System, Analytics, and Lessons Learned From a Real-World Project,” IEEE Internet of Things Journal, vol. 11, no. 3, pp. 4614–4627, 2024

    2024

  98. [110]

    Communication-Efficient Federated Learning for Digital Twin Edge Networks in Industrial IoT,

    Y . Lu, X. Huang, K. Zhang, S. Maharjan, and Y . Zhang, “Communication-Efficient Federated Learning for Digital Twin Edge Networks in Industrial IoT,”IEEE Transactions on Industrial Infor- matics, vol. 17, no. 8, pp. 5709–5718, 2021

    2021

  99. [111]

    Hypernetworks-Based Hierarchical Federated Learning on Hybrid Non-IID Datasets for Digital Twin in Industrial IoT,

    J. Yang, W. Jiang, and L. Nie, “Hypernetworks-Based Hierarchical Federated Learning on Hybrid Non-IID Datasets for Digital Twin in Industrial IoT,”IEEE Transactions on Network Science and Engineer- ing, vol. 11, no. 2, pp. 1413–1423, 2024

    2024

  100. [112]

    Digital Twin- Assisted Federated Learning Service Provisioning Over Mobile Edge Networks,

    R. Zhang, Z. Xie, D. Yu, W. Liang, and X. Cheng, “Digital Twin- Assisted Federated Learning Service Provisioning Over Mobile Edge Networks,”IEEE Transactions on Computers, vol. 73, no. 2, pp. 586– 598, 2024

    2024

  101. [113]

    Deep Reinforcement Learning for Network Energy Saving in 6G and Beyond Networks,

    D.-H. Tran, N. Van Huynh, S. Kaada, V . N. V o, E. Lagunas, and S. Chatzinotas, “Deep Reinforcement Learning for Network Energy Saving in 6G and Beyond Networks,”arXiv preprint arXiv:2408.10974, 2024

    2024 arXiv

  102. [114]

    Digital twin for intelligent context-aware iot healthcare systems,

    H. Elayan, M. Aloqaily, and M. Guizani, “Digital twin for intelligent context-aware iot healthcare systems,”IEEE Internet of Things Journal, vol. 8, no. 23, pp. 16 749–16 757, 2021

    2021

  103. [115]

    Fmcpnn in digital twins smart healthcare,

    Z. Yu, K. Wang, Z. Wan, S. Xie, and Z. Lv, “Fmcpnn in digital twins smart healthcare,”IEEE Consumer Electronics Magazine, vol. 12, no. 4, pp. 66–73, 2023

    2023

  104. [116]

    Digital-twin-enabled iomt system for surgical simulation using rac-gan,

    Y . Tai, L. Zhang, Q. Li, C. Zhu, V . Chang, J. J. P. C. Rodrigues, and M. Guizani, “Digital-twin-enabled iomt system for surgical simulation using rac-gan,”IEEE Internet of Things Journal, vol. 9, no. 21, pp. 20 918–20 931, 2022

    2022

  105. [117]

    A novel digital twin (dt) model based on wifi csi, signal processing and machine learning for patient respiration monitoring and decision-support,

    S. Khan, A. Alzaabi, Z. Iqbal, T. Ratnarajah, and T. Arslan, “A novel digital twin (dt) model based on wifi csi, signal processing and machine learning for patient respiration monitoring and decision-support,”IEEE Access, vol. 11, pp. 103 554–103 568, 2023

    2023

  106. [118]

    Cloud-based digital twinning for structural health monitoring using deep learning,

    H. V . Dang, M. Tatipamula, and H. X. Nguyen, “Cloud-based digital twinning for structural health monitoring using deep learning,”IEEE Transactions on Industrial Informatics, vol. 18, no. 6, pp. 3820–3830, 2022

    2022

  107. [119]

    Graph learning for cognitive digital twins in manufacturing systems,

    T. Mortlock, D. Muthirayan, S.-Y . Yu, P. P. Khargonekar, and M. Ab- dullah Al Faruque, “Graph learning for cognitive digital twins in manufacturing systems,”IEEE Transactions on Emerging Topics in Computing, vol. 10, no. 1, pp. 34–45, 2022

    2022

  108. [120]

    Machine-learning-driven digital twin for lifecycle management of complex equipment,

    Z. Ren, J. Wan, and P. Deng, “Machine-learning-driven digital twin for lifecycle management of complex equipment,”IEEE Transactions on Emerging Topics in Computing, vol. 10, no. 1, pp. 9–22, 2022

    2022

  109. [121]

    3d-amplifai: An ensemble machine learning approach to digital twin fault monitoring for additive manufacturing in smart factories,

    G. A. R. Sampedro, M. A. P. Putra, and M. Abisado, “3d-amplifai: An ensemble machine learning approach to digital twin fault monitoring for additive manufacturing in smart factories,”IEEE Access, vol. 11, pp. 64 128–64 140, 2023

    2023

  110. [122]

    Integrating machine learning model and digital twin system for additive manufacturing,

    N. Jyeniskhan, A. Keutayeva, G. Kazbek, M. H. Ali, and E. Shehab, “Integrating machine learning model and digital twin system for additive manufacturing,”IEEE Access, vol. 11, pp. 71 113–71 126, 2023

    2023

  111. [123]

    Resources-efficient adaptive federated learning for digital twin-enabled iiot,

    D. Qiao, M. Li, S. Guo, J. Zhao, and B. Xiao, “Resources-efficient adaptive federated learning for digital twin-enabled iiot,”IEEE Trans- actions on Network Science and Engineering, pp. 1–13, 2024

    2024

  112. [124]

    Real-world anomaly de- tection by using digital twin systems and weakly supervised learning,

    A. Castellani, S. Schmitt, and S. Squartini, “Real-world anomaly de- tection by using digital twin systems and weakly supervised learning,” IEEE Transactions on Industrial Informatics, vol. 17, no. 7, pp. 4733– 4742, 2021

    2021

  113. [125]

    A novel anomaly detection method for digital twin data using deconvolution operation with atten- tion mechanism,

    Z. Li, M. Duan, B. Xiao, and S. Yang, “A novel anomaly detection method for digital twin data using deconvolution operation with atten- tion mechanism,”IEEE Transactions on Industrial Informatics, vol. 19, no. 5, pp. 7278–7286, 2023

    2023

  114. [126]

    Dtitd: An intelligent insider threat detection framework based on digital twin and self-attention based deep learning models,

    Z. Q. Wang and A. El Saddik, “Dtitd: An intelligent insider threat detection framework based on digital twin and self-attention based deep learning models,”IEEE Access, vol. 11, pp. 114 013–114 030, 2023

    2023

  115. [127]

    On the need of neuromorphic twins to detect denial-of-service attacks on communication networks,

    H. Boche, R. F. Schaefer, H. V . Poor, and F. H. P. Fitzek, “On the need of neuromorphic twins to detect denial-of-service attacks on communication networks,”IEEE/ACM Transactions on Networking, pp. 1–13, 2024

    2024

  116. [128]

    Secure deep learning in defense in deep-learning-as-a-service computing systems in digital twins,

    Z. Lv, D. Chen, B. Cao, H. Song, and H. Lv, “Secure deep learning in defense in deep-learning-as-a-service computing systems in digital twins,”IEEE Transactions on Computers, vol. 73, no. 3, pp. 656–668, 2024

    2024

  117. [129]

    Blockchain and deep learning for secure communication in digital twin empowered industrial iot network,

    P. Kumar, R. Kumar, A. Kumar, A. A. Franklin, S. Garg, and S. Singh, “Blockchain and deep learning for secure communication in digital twin empowered industrial iot network,”IEEE Transactions on Network Science and Engineering, vol. 10, no. 5, pp. 2802–2813, 2023

    2023

  118. [130]

    A machine learning-based digital twin for anti-counterfeiting applications with copy detection patterns,

    Y . Belousov, G. Quétant, B. Pulfer, R. Chaban, J. Tutt, O. Taran, T. Holotyak, and S. V oloshynovskiy, “A machine learning-based digital twin for anti-counterfeiting applications with copy detection patterns,” IEEE Transactions on Information Forensics and Security, vol. 19, ...

    2024

  119. [131]

    Elite: An intelligent digital twin-based hierarchical routing scheme for softwarized vehicular networks,

    L. Zhao, Z. Bi, A. Hawbani, K. Yu, Y . Zhang, and M. Guizani, “Elite: An intelligent digital twin-based hierarchical routing scheme for softwarized vehicular networks,”IEEE Transactions on Mobile Computing, vol. 22, no. 9, pp. 5231–5247, 2023

    2023

  120. [132]

    Digital twin enhanced federated reinforcement learning with lightweight knowledge distillation in mo- bile networks,

    X. Zhou, X. Zheng, X. Cui, J. Shi, W. Liang, Z. Yan, L. T. Yang, S. Shimizu, and K. I.-K. Wang, “Digital twin enhanced federated reinforcement learning with lightweight knowledge distillation in mo- bile networks,”IEEE Journal on Selected Areas in Communications, vol. 41, no. ...

    2023

  121. [133]

    Ubiquitous 6G Service Through Non-Terrestrial Networks,

    J. Wigard, E. Juan, J. Stanczak, M. Lauridsen, A. Marcone, S. Hoppe, A. Ahmadzadeh, A. Masri, and D.-H. Tran, “Ubiquitous 6G Service Through Non-Terrestrial Networks,”IEEE Wireless Communications, vol. 30, no. 6, pp. 12–18, 2023

    2023

  122. [134]

    Emerging technologies for 6G non-terrestrial-networks: From academia to industrial applications,

    C. T. Nguyen, Y . M. Saputra, N. Van Huynh, T. N. Nguyen, D. T. Hoang, D. N. Nguyen, V .-Q. Pham, M. V oznak, S. Chatzinotas, and D.-H. Tran, “Emerging technologies for 6G non-terrestrial-networks: From academia to industrial applications,”IEEE Open Journal of the Communicatio...

    2024

  123. [135]

    Security-Reliability Trade- offs for Satellite–Terrestrial Relay Networks With a Friendly Jammer and Imperfect CSI,

    T. N. Nguyen, T. Van Chien, D.-H. Tran, V .-D. Phan, M. V oznak, S. Chatzinotas, Z. Ding, and H. V . Poor, “Security-Reliability Trade- offs for Satellite–Terrestrial Relay Networks With a Friendly Jammer and Imperfect CSI,”IEEE Transactions on Aerospace and Electronic Systems...

    2023

  124. [136]

    Security and reliability analysis of satellite-terrestrial multirelay networks with imperfect CSI,

    T. N. Nguyen, D.-H. Tran, T. Van Chien, V .-D. Phan, M. V oznak, and S. Chatzinotas, “Security and reliability analysis of satellite-terrestrial multirelay networks with imperfect CSI,”IEEE Systems Journal, 2022

    2022

  125. [137]

    Outage performance of satellite terrestrial full-duplex relaying networks with co-channel interference,

    T. N. Nguyen, L.-T. Tu, D.-H. Tran, V .-D. Phan, M. V oznak, S. Chatzinotas, and Z. Ding, “Outage performance of satellite terrestrial full-duplex relaying networks with co-channel interference,”IEEE Wireless Communications Letters, vol. 11, no. 7, pp. 1478–1482, 2022

    2022

  126. [138]

    A hi- erarchical digital twin network for satellite communication networks,

    Y . Zhou, R. Zhang, J. Liu, T. Huang, Q. Tang, and F. R. Yu, “A hi- erarchical digital twin network for satellite communication networks,” IEEE Communications Magazine, vol. 61, no. 11, pp. 104–110, 2023

    2023

  127. [139]

    In- terlink: A digital twin-assisted storage strategy for satellite-terrestrial networks,

    L. Zhao, C. Wang, K. Zhao, D. Tarchi, S. Wan, and N. Kumar, “In- terlink: A digital twin-assisted storage strategy for satellite-terrestrial networks,”IEEE Transactions on Aerospace and Electronic Systems, vol. 58, no. 5, pp. 3746–3759, 2022

    2022

  128. [140]

    Dynamic digital twin and online scheduling for contact window resources in satellite network,

    H. Fan, J. Long, L. Liu, and Z. Yang, “Dynamic digital twin and online scheduling for contact window resources in satellite network,”IEEE Transactions on Industrial Informatics, vol. 19, no. 5, pp. 7217–7227, 2023

    2023

  129. [141]

    A dt machine learning-based satellite orbit prediction for iot applications,

    X. Xu, H. Wen, H. Song, and Y . Zhao, “A dt machine learning-based satellite orbit prediction for iot applications,”IEEE Internet of Things Magazine, vol. 6, no. 2, pp. 96–100, 2023

    2023

  130. [142]

    Throughput maxi- mization for backscatter-and cache-assisted wireless powered UA V technology,

    D.-H. Tran, S. Chatzinotas, and B. Ottersten, “Throughput maxi- mization for backscatter-and cache-assisted wireless powered UA V technology,”IEEE Transactions on Vehicular Technology, vol. 71, no. 5, pp. 5187–5202, 2022

    2022

  131. [143]

    Satellite-and cache-assisted UA V: A joint cache placement, resource allocation, and trajectory optimization for 6G aerial networks,

    ——, “Satellite-and cache-assisted UA V: A joint cache placement, resource allocation, and trajectory optimization for 6G aerial networks,” IEEE Open Journal of Vehicular Technology, vol. 3, pp. 40–54, 2022

    2022

  132. [144]

    Toward intelligent cooperation of uav swarms: When machine learning meets digital twin,

    L. Lei, G. Shen, L. Zhang, and Z. Li, “Toward intelligent cooperation of uav swarms: When machine learning meets digital twin,”IEEE Network, vol. 35, no. 1, pp. 386–392, 2021

    2021

  133. [145]

    Dynamic Digital Twin and Federated Learning With Incentives for Air-Ground 40 Networks,

    W. Sun, N. Xu, L. Wang, H. Zhang, and Y . Zhang, “Dynamic Digital Twin and Federated Learning With Incentives for Air-Ground 40 Networks,”IEEE Transactions on Network Science and Engineering, vol. 9, no. 1, pp. 321–333, 2022

    2022

  134. [146]

    Lightweight Digital Twin and Federated Learning With Distributed Incentive in Air-Ground 6G Networks,

    W. Sun, S. Lian, H. Zhang, and Y . Zhang, “Lightweight Digital Twin and Federated Learning With Distributed Incentive in Air-Ground 6G Networks,”IEEE Transactions on Network Science and Engineering, vol. 10, no. 3, pp. 1214–1227, 2023

    2023

  135. [147]

    Digital-Twin-Assisted Task Assignment in Multi-UA V Systems: A Deep Reinforcement Learning Approach,

    X. Tang, X. Li, R. Yu, Y . Wu, J. Ye, F. Tang, and Q. Chen, “Digital-Twin-Assisted Task Assignment in Multi-UA V Systems: A Deep Reinforcement Learning Approach,”IEEE Internet of Things Journal, vol. 10, no. 17, pp. 15 362–15 375, 2023

    2023

  136. [148]

    Hybrid Machine Learning Approach for Resource Allocation of Digital Twin in UA V- Aided Internet-of-Vehicles Networks,

    B. Hazarika, K. Singh, A. Paul, and T. Q. Duong, “Hybrid Machine Learning Approach for Resource Allocation of Digital Twin in UA V- Aided Internet-of-Vehicles Networks,”IEEE Transactions on Intelli- gent Vehicles, vol. 9, no. 1, pp. 2923–2939, 2024

    2024

  137. [149]

    Adaptive multi-uav trajectory planning leveraging digital twin technol- ogy for urban iiot applications,

    L. Zhao, S. Li, Y . Guan, S. Wan, A. Hawbani, Y . Bi, and M. Guizani, “Adaptive multi-uav trajectory planning leveraging digital twin technol- ogy for urban iiot applications,”IEEE Transactions on Network Science and Engineering, vol. 11, no. 6, pp. 5349–5363, 2024

    2024

  138. [150]

    Deep reinforcement learning for flocking motion of multi-uav systems: Learn from a digital twin,

    G. Shen, L. Lei, Z. Li, S. Cai, L. Zhang, P. Cao, and X. Liu, “Deep reinforcement learning for flocking motion of multi-uav systems: Learn from a digital twin,”IEEE Internet of Things Journal, vol. 9, no. 13, pp. 11 141–11 153, 2022

    2022

  139. [151]

    Digital-twin-assisted task assignment in multi-uav systems: A deep reinforcement learning approach,

    X. Tang, X. Li, R. Yu, Y . Wu, J. Ye, F. Tang, and Q. Chen, “Digital-twin-assisted task assignment in multi-uav systems: A deep reinforcement learning approach,”IEEE Internet of Things Journal, vol. 10, no. 17, pp. 15 362–15 375, 2023

    2023

  140. [152]

    Dynamic multi-objective awpso in dt-assisted uav cooperative task assignment,

    M. Deng, Z. Yao, X. Li, H. Wang, A. Nallanathan, and Z. Zhang, “Dynamic multi-objective awpso in dt-assisted uav cooperative task assignment,”IEEE Journal on Selected Areas in Communications, vol. 41, no. 11, pp. 3444–3460, 2023

    2023

  141. [153]

    A federated digital twin framework for uavs-based mobile scenarios,

    L. Zhou, S. Leng, and Q. Wang, “A federated digital twin framework for uavs-based mobile scenarios,”IEEE Transactions on Mobile Com- puting, vol. 23, no. 6, pp. 7377–7393, 2024

    2024

  142. [154]

    A proof of concept on digital twin-controlled wifi core network selection for in-flight connectivity,

    T. Bilen, E. Ak, B. Bal, and B. Canberk, “A proof of concept on digital twin-controlled wifi core network selection for in-flight connectivity,” IEEE Communications Standards Magazine, vol. 6, no. 3, pp. 60–68, 2022

    2022

  143. [155]

    Darius: A digital twin to improve the performance of quantum key distribution,

    M. Ahmadian, M. Ruiz, J. Comellas, and L. Velasco, “Darius: A digital twin to improve the performance of quantum key distribution,”Journal of Lightwave Technology, vol. 42, no. 5, pp. 1356–1367, 2024

    2024

  144. [156]

    Portable Frequency Sta- bilized Lasers for Quantum Technologies Using Digital Techniques,

    H. K. Chau, C. Bridges, and P. Nisbet-Jones, “Portable Frequency Sta- bilized Lasers for Quantum Technologies Using Digital Techniques,” IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–6, 2023

    2023

  145. [157]

    What can we expect from Quantum (Dig- ital) Twins?

    M. Amir, C. Bauckhage, A. Chircu, C. Czarnecki, C. Knopf, N. Pi- atkowski, and E. Sultanow, “What can we expect from Quantum (Dig- ital) Twins?”17th International Conference on Wirtschaftsinformatik, WI 2022, 2022

    2022

  146. [158]

    Toward Smart Manufacturing Using Spiral Digital Twin Framework and Twinchain,

    A. Khan, F. Shahid, C. Maple, A. Ahmad, and G. Jeon, “Toward Smart Manufacturing Using Spiral Digital Twin Framework and Twinchain,” IEEE Transactions on Industrial Informatics, vol. 18, no. 2, pp. 1359– 1366, 2022

    2022

  147. [159]

    Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning,

    N. Van Huynh, B. Zhang, D.-H. Tran, D. T. Hoang, D. N. Nguyen, G. Zheng, D. Niyato, and Q.-V . Pham, “Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning,”arXiv preprint arXiv:2410.17971, 2024

    arXiv

  148. [160]

    Ab-initio tree- tensor-network digital twin for quantum computer benchmarking in 2d,

    D. Jaschke, A. Pagano, S. Weber, and S. Montangero, “Ab-initio tree- tensor-network digital twin for quantum computer benchmarking in 2d,”arXiv preprint arXiv:2210.03763, 2022

    2022 arXiv

  149. [161]

    Designing a digital twin for quantum key distribution,

    M. Ahmadian, M. Ruiz, M. On, S. K. Singh, J. Comellas, R. Proietti, S. B. Yoo, and L. Velasco, “Designing a digital twin for quantum key distribution,” in2022 European Conference on Optical Communication (ECOC). IEEE, 2022, pp. 1–4

    2022

  150. [162]

    Dtqfl: A digital twin- assisted quantum federated learning algorithm for intelligent diagnosis in 5g mobile network,

    Z. Qu, Y . Li, B. Liu, D. Gupta, and P. Tiwari, “Dtqfl: A digital twin- assisted quantum federated learning algorithm for intelligent diagnosis in 5g mobile network,”IEEE journal of biomedical and health infor- matics, 2023

    2023

  151. [163]

    Efficient resource allocation for building the metaverse with uavs: A quantum collective reinforcement learning approach,

    Y . Wang, Y . He, F. R. Yu, B. Song, and V . C. Leung, “Efficient resource allocation for building the metaverse with uavs: A quantum collective reinforcement learning approach,”IEEE Wireless Communications, vol. 30, no. 5, pp. 152–159, 2023

    2023

  152. [164]

    Integration of quantum technologies into metaverse: Applications, potentials, and challenges,

    E. A. Tuli, J.-M. Lee, and D.-S. Kim, “Integration of quantum technologies into metaverse: Applications, potentials, and challenges,” IEEE Access, vol. 12, pp. 29 995–30 019, 2024

    2024

  153. [165]

    Post-quantum hybrid digital sig- natures with hardware-support for digital twins,

    S. E. Nouma and A. A. Yavuz, “Post-quantum hybrid digital sig- natures with hardware-support for digital twins,”arXiv preprint arXiv:2305.12298, 2023

    2023 arXiv

  154. [166]

    Quantum computing for climate change detection, climate modeling, and climate digital twin,

    S. Otgonbaatar, O. Nurmi, M. Johansson, J. Mäkelä, P. Gawron, Z. Puchała, J. Mielzcarek, A. Miroszewski, C. O. Dumitruet al., “Quantum computing for climate change detection, climate modeling, and climate digital twin,”Authorea Preprints, 2023

    2023

  155. [167]

    Quantum Semantic Communications for Metaverse: Principles and Challenges,

    U. Khalid, M. S. Ulum, A. Farooq, T. Q. Duong, O. A. Dobre, and H. Shin, “Quantum Semantic Communications for Metaverse: Principles and Challenges,”IEEE Wireless Communications, vol. 30, no. 4, pp. 26–36, 2023

    2023

  156. [168]

    Service for deploying digital twins of qkd networks,

    R. Martin, B. Lopez, I. Vidal, F. Valera, and B. Nogales, “Service for deploying digital twins of qkd networks,”Applied Sciences, vol. 14, no. 3, p. 1018, 2024

    2024

  157. [169]

    Softwarized resource allocation in digital twins-empowered networks for future quantum-enabled consumer applications,

    H. Cao, S. Garg, S. Mumtaz, M. Alrashoud, L. Yang, and G. Kaddoum, “Softwarized resource allocation in digital twins-empowered networks for future quantum-enabled consumer applications,”IEEE Transactions on Consumer Electronics, 2024

    2024

  158. [170]

    Digital twin placement for minimum application request delay with data age targets,

    M. Vaezi, K. Noroozi, T. D. Todd, D. Zhao, and G. Karakostas, “Digital twin placement for minimum application request delay with data age targets,”IEEE Internet of Things Journal, vol. 10, no. 13, pp. 11 547– 11 557, Jul. 2023

    2023

  159. [171]

    Data synchronization in vehicular digital twin network: A game theoretic approach,

    J. Zheng, T. H. Luan, Y . Zhang, R. Li, Y . Hui, L. Gao, and M. Dong, “Data synchronization in vehicular digital twin network: A game theoretic approach,”IEEE Transactions on Wireless Communications, vol. 22, no. 11, pp. 7635–7647, Nov. 2023

    2023

  160. [172]

    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,”arXiv preprint arXiv:2301.11283, 2023

    2023 arXiv

  161. [173]

    6g digital twin networks: From theory to practice,

    X. Lin, L. Kundu, C. Dick, E. Obiodu, T. Mostak, and M. Flaxman, “6g digital twin networks: From theory to practice,”IEEE Communications Magazine, vol. 61, no. 11, pp. 72–78, 2023

    2023

  162. [174]

    Explainable ai for 6g use cases: Technical aspects and research challenges,

    S. Wang, M. A. Qureshi, L. Miralles-Pechuán, T. Huynh-The, and M. Liyanage, “Explainable ai for 6g use cases: Technical aspects and research challenges,”IEEE Open Journal of the Communications Society, vol. 5, pp. 1–18, 2024

    2024

  163. [175]

    Digital twin: A comprehensive survey of security threats,

    C. Alcaraz and J. Lopez, “Digital twin: A comprehensive survey of security threats,”IEEE Communications Surveys & Tutorials, vol. 24, no. 3, pp. 1475–1503, 2022

    2022

  164. [176]

    Two-timescale synchronization and migration for digital twin net- works: A multi-agent deep reinforcement learning approach,

    W. Liu, Y . Fu, Y . Guo, F. Wang, W. Sun, and Y . Zhang, “Two-timescale synchronization and migration for digital twin net- works: A multi-agent deep reinforcement learning approach,”IEEE Transactions on Wireless Communications, 2024, early Access, doi:10.1109/TWC.2024.3452689

    2024

  165. [2024]

    Available: https://doi.org/10.1117/1.AP.6.2.026006

    [Online]. Available: https://doi.org/10.1117/1.AP.6.2.026006

    doi

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.