True-data testbed for 5G/B5G intelligent network

doi:10.23919/ICN.2021.0002

2021, Vol. 2

Issue (2): 133-149 doi: 10.23919/ICN.2021.0002

Regular Articles

True-data testbed for 5G/B5G intelligent network

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

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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.

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

Received: 23 October 2020 Online: 19 August 2021

Fund: National Key R&D Program of China(2018YFB1800801);National Natural Science Foundation of China(61720106003)

Corresponding Authors: Xiaohu You E-mail: huangym@seu.edu.cn;s.liu@seu.edu.cn;zhangcheng_seu@seu.edu.cn;xhyu@seu.edu.cn;wuhq@cae.cn

About author: Yongming Huang received the BS and MS degrees from Nanjing University, Nanjing, China, in 2000 and 2003, respectively, and the PhD degree in electrical engineering from Southeast University, Nanjing, China, in 2007.|Since March 2007, he has been a faculty at the School of Information Science and Engineering, Southeast University, China, where he is currently a full professor. During 2008-2009, he visited the Signal Processing Lab, Royal Institute of Technology (KTH), Stockholm, Sweden. His current research interests include intelligent 5G/6G mobile communications and millimeter wave wireless communications. He has published over 200 peer-reviewed papers, and holds over 80 invention patents. He submitted around 20 technical contributions to IEEE standards, and was awarded a certificate of appreciation for outstanding contribution to the development of IEEE standard 802.11aj. He served as an associate editor for the IEEE Transactions on Signal Processing and a guest editor for the IEEE Journal Selected Areas in Communications. He is currently an editor-at-large for the IEEE Open Journal of the Communications Society and an associate editor for the IEEE Wireless Communications Letters.|Shengheng Liu received the BEng and PhD degrees in electronics engineering from Beijing Institute of Technology, Beijing, China, in 2010 and 2017, respectively.|He is currently an associate professor with the School of Information Science and Engineering, Southeast University (SEU), Nanjing, China. Prior to joining SEU, he held a postdoctoral position at the Institute for Digital Communications, the University of Edinburgh, Edinburgh, UK, from 2017 to 2018. He also worked as a visiting research associate from 2015 to 2016 at the Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA, USA, under the support of the China Scholarship Council. He is a recipient of the 2017 National Excellent Doctoral Dissertation Award from the China Institute of Communications. His research interests mainly focus on intelligent sensing and wireless communications.|Cheng Zhang received the BEng degree from Sichuan University, Chengdu, China, in 2009, the MSc degree from Xi’an Electronic Engineering Research Institute (EERI), Xi’an, China, in 2012, and the PhD degree from Southeast University, Nanjing, China, in 2018. From 2012 to 2013, he was a radar signal processing engineer with Xi’an EERI. From November 2016 to November 2017, he was a visiting student with the University of Alberta, Edmonton, AB, Canada. He is currently an assistant professor with Southeast University (SEU), and supported by the Zhishan Young Scholar Program of SEU. His research interests include MIMO wireless communications and signal processing. He won the Excellent Doctoral Dissertation of the China Education Society of Electronics, in December 2019.|Xiaohu You received the BS, MS, and PhD degrees in electrical engineering from Southeast University, Nanjing, China, in 1982, 1985, and 1988, respectively. Since 1990, he has been working with National Mobile Communications Research Laboratory at Southeast University, where now he holds the rank of director and professor. He has contributed over 300 IEEE journal papers and 3 books in the areas of signal processing and wireless communications. From 1999 to 2002, he was the principal expert of the C3G Project. From 2001-2006, he was the principal expert of the China National 863 Beyond 3G FuTURE Project. Since 2013, he has been the principal investigator of China National 863 5G Project.|He served as the general chair of IEEE WCNC 2013, IEEE VTC 2016 Spring, and IEEE ICC 2019. Now he is the secretary general of the FuTURE Forum, vice chair of China IMT-2020 (5G) Promotion Group, and vice chair of China National Mega Project on New Generation Mobile Network. He was the recipient of the National 1st Class Invention Prize in 2011, and he was elected as IEEE Fellow for leadership in the development of mobile communications in China in the same year.|Hequan Wu received the BEng degree from the Wuhan Post and Telecommunications Institute, in 1964. He has been with the China Academy of Post and Telecommunications, Ministry of Posts and Telecommunications, since 1964. He was the vice president and chief engineer of the China Academy of Telecommunications Technology from 1997 to 2003. He studied optical fiber transmission systems and broadband networks, and managed a series of national research and development projects. In recent years, he has focused on the development strategy of NGN and NGI as well as 3G, 4G, and 5G. He was elected as an academician of the Chinese Academy of Engineering in 1999 and has been the vice president of CAE from 2002 to 2010. He is currently serving as the vice director of the Advisory Committee for the State Informatization of China as well as the president of the Internet Society of China, the vice director of the Executive Council of the China Institute of Communications, and the Chinese Institute of Electronics, respectively. He is the director of the Communication S&T Committee of MII and a member of the Advisory Committee of the National Basic Research Program of China. He serves as the director of the Experts Committee of China’s Next Generation Internet Project.


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Cite this article:

Yongming Huang,Shengheng Liu,Cheng Zhang,Xiaohu You,Hequan Wu. True-data testbed for 5G/B5G intelligent network. , 2021, 2: 133-149.

URL:

http://icn.tsinghuajournals.com/10.23919/ICN.2021.0002 OR http://icn.tsinghuajournals.com/Y2021/V2/I2/133

Table 1 Comparisons of the proposed TTIN with the state-of-the-art 5G experimental networks.

Fig. 1 Typical 5G applications.

Fig. 2 System architecture of TTIN.

Fig. 3 Functional hierarchical framework of TTIN.

Table 2 Configurations of the 5G radio access network.

Performance parameter	Description
Uplink peak rate/user	$261 ? Mbps$
Downlink peak rate/user	$1.18 ? Gbps$
Uplink user experience data rate	$170 ? Mbps$
Downlink user experience data rate	$940 ? Mbps$
End-to-end delay	$14 ? ms$
Success rate of handover	100%
Traffic density	$1.045 ? Tbps / km 2$

Table 3 Key performance parameters of the 5G experimental network.

Fig. 4 Framework of the data acquisition platform.

Table 4 Typical network data collected in the DAP.

Supported function	Description
Supported language	C/C++/Java/Python…
Integrated development environments (IDEs)	VS/Pycharm/Anaconda…
Deep learning framework	Tensorflow/Pytorch/PaddlePaddle/Mxnet…
Pre-trained model	VGG 16, VGG 19, Inception V1 $-$ V4, ResNet 50 $-$ 101 $-$ 152, MobileNet V1 $-$ V2, BERT, Transformer, GPT $-$ 2 …

Table 5 Supported functions of AI cloud platform.

Fig. 5 Schematic diagram of the intelligent throughput optimization approach.

Table 6 Details of AI-empowered throughput optimization.

Fig. 6 Experiment scene of TTIN road test in CWV.

Fig. 7 Real-data experiment results of average throughput optimization.

Fig. 8 Illustration of the intelligent massive MIMO optimization scheme.

Table 7 Details of AI-empowered massive MIMO.

Fig. 9 Illustration of intelligent interference coordination approach.

Table 8 Details of AI-empowered interference coordination.

Fig. 10 Decision model of energy conservation strategy.

Table 9 Details of AI-empowered energy conservation.


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