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Intelligent and Converged Networks 2021, 2(2): 133-149 https://doi.org/10.23919/ICN.2021.0002
Open Access | Issue | Published: 30 June 2021

True-data testbed for 5G/B5G intelligent network

Show Author's Information Yongming Huang,  Shengheng Liu,  Cheng Zhang,  Xiaohu You*( ),  Hequan Wu
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
China Information and Communication Technology Group Corporation, Beijing 100083, China
Purple Mountain Laboratories, Nanjing 211111, China
Keywords:
big data, true-data testbed, wireless communication networks, artificial intelligence (AI), internet of everything (IoE)
Cite this article:
Huang Y, Liu S, Zhang C, et al. True-data testbed for 5G/B5G intelligent network. Intelligent and Converged Networks, 2021, 2(2): 133-149. https://doi.org/10.23919/ICN.2021.0002
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Abstract Full text About this article

Future beyond fifth-generation (B5G) and sixth-generation (6G) mobile communications will shift from facilitating interpersonal communications to supporting internet of everything (IoE), where intelligent communications with full integration of big data and artificial intelligence (AI) will play an important role in improving network efficiency and providing high-quality service. As a rapid evolving paradigm, the AI-empowered mobile communications demand large amounts of data acquired from real network environment for systematic test and verification. Hence, we build the world’s first true-data testbed for 5G/B5G intelligent network (TTIN), which comprises 5G/B5G on-site experimental networks, data acquisition & data warehouse, and AI engine & network optimization. In the TTIN, true network data acquisition, storage, standardization, and analysis are available, which enable system-level online verification of B5G/6G-orientated key technologies and support data-driven network optimization through the closed-loop control mechanism. This paper elaborates on the system architecture and module design of TTIN. Detailed technical specifications and some of the established use cases are also showcased.


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About this article

True-data testbed for 5G/B5G intelligent network

Show Author's information Yongming Huang, Shengheng Liu, Cheng Zhang, Xiaohu You*( ), Hequan Wu
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
China Information and Communication Technology Group Corporation, Beijing 100083, China
Purple Mountain Laboratories, Nanjing 211111, China

Abstract

Future beyond fifth-generation (B5G) and sixth-generation (6G) mobile communications will shift from facilitating interpersonal communications to supporting internet of everything (IoE), where intelligent communications with full integration of big data and artificial intelligence (AI) will play an important role in improving network efficiency and providing high-quality service. As a rapid evolving paradigm, the AI-empowered mobile communications demand large amounts of data acquired from real network environment for systematic test and verification. Hence, we build the world’s first true-data testbed for 5G/B5G intelligent network (TTIN), which comprises 5G/B5G on-site experimental networks, data acquisition & data warehouse, and AI engine & network optimization. In the TTIN, true network data acquisition, storage, standardization, and analysis are available, which enable system-level online verification of B5G/6G-orientated key technologies and support data-driven network optimization through the closed-loop control mechanism. This paper elaborates on the system architecture and module design of TTIN. Detailed technical specifications and some of the established use cases are also showcased.

Keywords: big data, true-data testbed, wireless communication networks, artificial intelligence (AI), internet of everything (IoE)

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Publication history
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Publication history

Received: 23 October 2020
Revised: 27 December 2020
Accepted: 13 January 2021
Published: 30 June 2021
Issue date: June 2021

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© ITU and TUP 2021

Acknowledgements

Acknowledgmen This work was supported in part by the National Key R&D Program of China (No. 2018YFB1800801) and the National Natural Science Foundation of China (Nos. 61720106003 and 62001103).

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This work is available under the CC BY-NC-ND 3.0 IGO license: https://creativecommons.org/licenses/by-nc-nd/3.0/igo/.

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