Abstract
Although the fifth-generation wireless communication network (5G) has made much progress in improving the quality of user experience by providing large bandwidth transmission, it only provides the connectivity services between user equipments (UEs) and the network. With the sensing and intelligence are envisioned to become the native capability of the sixth-generation communication network (6G), there is an urgent need for a new network architecture enabling the “on-path-data-processing”, to make better leverage of distributed and ubiquitous computation resources and data. Thus, we propose a Data Plane in 6G network, which is independent of existing User Plane, aiming at constructing data pipelines based on various data service requirements. It systematically provides the collaboration of data among multiple network components with arbitrary topology with the support for on-path-data-processing. Based on this, we propose three data forwarding control protocols, guaranteeing the operation of Data Plane by providing data forwarding in any topology. Simulation experiments demonstrate the good scalability and efficiency of the three protocols in Data Plane.
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References
(2019) Near realtime RAN intelligent controller (RIC). https://docs.o-ran-sc.org/en/latest/projects.html#near-realtime-ran-intelligent-controller-ric
(2020) Framework for data handling to enable machine learning in future networks including IMT-2020. https://www.itu.int/rec/dologin_pub.asp?lang=e&id=T-REC-Y.3174-202002-I!!PDF-E&type=items
IEEE standard for framework of blockchain-based internet of things (IoT) data management (2021) IEEE Std 2144.1-2020, 1–20. https://doi.org/10.1109/IEEESTD.2021.9329260
TS 23.288 (2022). https://www.3gpp.org/ftp/Specs/archive/23_series/23.288
TS 28.104 (2022). https://www.3gpp.org/ftp/Specs/archive/28_series/28.104
3GPP: TS 138 300 - v15.3.1 - 5G; NR; Overall description (2022). https://www.etsi.org/deliver/etsi_ts/138300_138399/138300/15.03.01_60/ts_138300v150301p.pdf
Baumann D (2014) Minimization of drive tests (MDT) in mobile communication networks. In: Proceeding zum seminar future internet (FI) und innovative internet technologien und mobilkommunikation (IITM), vol 9, p 7
Chowdhury MZ, Shahjalal M, Ahmed S, Jang YM (2020) 6G wireless communication systems: applications, requirements, technologies, challenges, and research directions. IEEE Open Journal of the Communications Society 1:957–975
ETSI (2019) Zero-touch network and service management (ZSM); reference architecture Group Specification (GS) ETSI GS ZSM 2
(2021) IMT-2030 (6G) Promotion Group: 6G vision and candidate technologies. http://www.caict.ac.cn/english/news/202106/P020210608349616163475.pdf
Letaief KB, Shi Y, Lu J, Lu J (2021) Edge artificial intelligence for 6G: vision, enabling technologies, and applications. IEEE J Sel Areas Commun 40(1):5–36
Li Y, Huang J, Sun Q, Sun T, Wang S (2021) Cognitive service architecture for 6G core network. IEEE Trans Industr Inf 17(10):7193–7203
Liu F, Cui Y, Masouros C, Xu J, Han TX, Eldar YC, Buzzi S (2022) Integrated sensing and communications: Towards dual-functional wireless networks for 6G and beyond. IEEE Journal on Selected Areas in Communications
McMahan B, Moore E, Ramage D, Hampson S, Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data. In: International conference on artificial intelligence and statistics, vol 54, pp 1273–1282
Nguyen VL, Lin PC, Cheng BC, Hwang RH, Lin YD (2021) Security and privacy for 6G: a survey on prospective technologies and challenges. IEEE Communications Surveys & Tutorials 23(4):2384–2428
Noor TH, Sheng QZ (2011) Trust as a service: a framework for trust management in cloud environments. In: International conference on web information systems engineering. Springer, pp 314–321
Roeland D, Raizer K, Berggren V, Öhlén P, Linder N (2022) Cognitive networks - towards an end-to-end 6G architecture. https://www.ericsson.com/en/blog/2022/1/cognitive-networks-6g-architecture
Shafagh H, Burkhalter L, Hithnawi A, Duquennoy S (2017) Towards blockchain-based auditable storage and sharing of iot data. In: Proceedings of the 2017 on cloud computing security workshop, pp 45–50
Shen XS, Huang C, Liu D, Xue L, Zhuang W, Sun R, Ying B (2021) Data management for future wireless networks: architecture, privacy preservation, and regulation. IEEE Netw 35(1):8–15
Talwar S, Himayat N, Nikopour H, Xue F, Wu G, Ilderem V (2021) 6G: Connectivity in the era of distributed intelligence. IEEE Commun Mag 59(11):45–50
Next G Alliance: Next G Alliance Report: 6G Distributed Cloud and Communications System (2022). https://circle.cloudsecurityalliance.org/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=fdfd916b-050c-4792-9056-f878617b478d
One6G Association: 6G Technology Overview (2022). https://zenodo.org/record/6630706/files/one6G_Technology_overview_WhitePaper_June22.pdf?download=1
Tong W, Li GY (2022) Nine challenges in artificial intelligence and wireless communications for 6G. IEEE Wireless Communications
Tong W, Zhu P (2022) 6G: The next horizon. TITLES 54
Wang M, Zhu T, Zhang T, Zhang J, Yu S, Zhou W (2020) Security and privacy in 6G networks: New areas and new challenges. Digital Communications and Networks 6(3):281–291
Wang S, Dinh TTA, Lin Q, Xie Z, Zhang M, Cai Q, Chen G, Ooi BC, Ruan P (2018) Forkbase: An efficient storage engine for blockchain and forkable applications. Proceedings of the VLDB Endowment 11(10)
Wang S, Sun T, Yang H, Duan X, Lu L (2020) 6G network: Towards a distributed and autonomous system. In: 2020 2Nd 6g wireless summit (6g SUMMIT). IEEE, pp 1–5
Xiao Y, Shi G, Krunz M (2020) Towards ubiquitous AI in 6G with federated learning. arXiv:2004.13563
Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology 10(2):1–19
Zou G, Qin Z, Deng S, Li KC, Gan Y, Zhang B (2021) Towards the optimality of service instance selection in mobile edge computing. Knowl-Based Syst 217:106831
Funding
This work was supported in part by the National Science Foundation of China under Grants U20A20173 and 62125206, and the Key Research Project of Zhejiang Province under Grant 2022C01145.
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Zhen Qin, Xueqiang Yan, Mingyu Zhao, Yan Xi, Lu Lu, Tao Sun and Nanxiang Shi made contributions to the concept and design of the article. Moreover, Zhen Qin, Xueqiang Yan and Mingyu Zhao performed paper writting and analysis, and Yan Xi conducted the experiments. Shuiguang Deng and Jianjun Wu reviewed and corrected the concept, design and paper writing.
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Qin, Z., Deng, S., Yan, X. et al. 6G Data Plane: A Novel Architecture Enabling Data Collaboration with Arbitrary Topology. Mobile Netw Appl 28, 394–405 (2023). https://doi.org/10.1007/s11036-023-02093-y
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DOI: https://doi.org/10.1007/s11036-023-02093-y