Investigation and Comparison of 5G Channel Models: From QuaDRiGa, NYUSIM, and MG5G Perspectives

PANG Lihua; ZHANG Jin; ZHANG Yang; HUANG Xinyi; CHEN Yijian; LI Jiandong

doi:10.1049/cje.2021.00.103

Volume 31 Issue 1

Jan. 2022

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2022 > 31(1): 1-17

PANG Lihua, ZHANG Jin, ZHANG Yang, et al. “Investigation and Comparison of 5G Channel Models: From QuaDRiGa, NYUSIM, and MG5G Perspectives”. Chinese Journal of Electronics, vol. 31 no. 1. doi: 10.1049/cje.2021.00.103

Citation:

PANG Lihua, ZHANG Jin, ZHANG Yang, et al. “Investigation and Comparison of 5G Channel Models: From QuaDRiGa, NYUSIM, and MG5G Perspectives”. Chinese Journal of Electronics, vol. 31 no. 1. doi: 10.1049/cje.2021.00.103

Citation:

PDF( 9052 KB)

Investigation and Comparison of 5G Channel Models: From QuaDRiGa, NYUSIM, and MG5G Perspectives

doi: 10.1049/cje.2021.00.103

PANG Lihua^{1, 2
,},
ZHANG Jin^2
,,
ZHANG Yang^{2, 3
,},
HUANG Xinyi^2
,,
CHEN Yijian^4
,,
LI Jiandong^2
,

1.
School of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
2.
State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China
3.
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
4.
Algorithm Department, ZTE Corporation, Shenzhen 518057, China

Funds: This work was supported in part by the National Natural Science Foundation of China (61871300, 61701392, U19B2015), the Key Research and Development Program of Shaanxi (2021GY-050, 2019ZDLSF07-06), the Excellent Youth Science Foundation of Xi’an University of Science and Technology (2019YQ3-13), the Fundamental Research Funds for the Central Universities (JB210112), and the Open Research Fund of the National Mobile Communications Research Laboratory (2019D12).

More Information

Author Bio:
received the B.E., M.S. and Ph.D. degrees from Xidian University, Xi’an, China, in 2006, 2009, and 2013, respectively, all in electrical engineering. She is currently an Associate Professor with the School of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an, China. Her research interests include signal processing for wireless communications, stochastic network optimization, and network performance analysis. (Email: lhpang.xidian@gmail.com)

received the B.E. degree in information security from Xidian University, Xi’an, China, in 2018. She is currently working toward the M.S. degree with the School of Telecommunications Engineering, Xidian University, Xi’an, China. Her research interests include channel measurement and modeling for millimeter-wave wireless communications. (Email: 844194003@qq.com)

(corresponding author) received the Ph.D. degree in electrical engineering from Xidian University, Xi’an, China, in 2011. During 2009 to 2010, he was a Visiting Scholar with the Department of Electrical and Computer Engineering, University of California, Davis, CA, USA. After working as a Research Engineer at Huawei Technologies, he rejoined Xidian University in 2013 and is currently an Associate Professor. His main area of research includes wireless channel measurement and modeling, signal processing for massive MIMO systems, green communications, and resource allocation strategies. (Email: yangzhang1984@gmail.com)

received the B.E. degree in communications engineering from Xidian University, Xi’an, China, in 2019. She is currently working toward the M.S. degree with the School of Telecommunications Engineering, Xidian University. Her current research interest is massive MIMO channel modeling for 5G wireless communications. (Email: 1412684190@qq.com)

received the B.S. degree in automation from Central South University, Changsha, China, in 2006. He is currently a Senior Engineer with the ZTE Corporation, Shenzhen, China. His research interests include intelligent electromagnetic surface, orbital angular momentum-based communications, and cell free networks. (Email: chen.yijian@zte.com.cn)

received the B.E., M.S. and Ph.D. degrees in electrical engineering from Xidian University, Xi’an, China, in 1982, 1985, and 1991, respectively. Since 1985, he has been with Xidian University, where he has been a Professor since 1994. From 2002 to 2003, he was a Visiting Professor with the Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA. His current research interests include mobile communications, broadband wireless systems, ad hoc networks, cognitive and software radio, self-organizing networks, and game theory for wireless networks. (Email: jdli@xidian.edu.cn)
Received Date: 2021-03-27
Accepted Date: 2021-05-19

Available Online: 2021-09-14

Publish Date: 2022-01-05

Abstract

Abstract

This paper investigates and compares three channel models for the fifth generation (5G) wireless communications: the quasi deterministic radio channel generator (QuaDRiGa), the NYUSIM channel simulator model developed by New York University, and the more general 5G (MG5G) channel model. First, the characteristics of the modeling processes of the three models are introduced from the perspective of model framework. Then, the small-scale parameter modeling strategies of the three models are compared from space/time/frequency domains as well as polarization aspect. In particular, the drifting of small-scale parameters is introduced in detail. Finally, through the simulation results of angular power spectrum, Doppler power spectrum density, temporal autocorrelation function, power delay profile, frequency correlation function, channel capacity, and eigenvalue distribution, the three models are comprehensively investigated. According to the simulation results, we clearly analyze the impact of the modeling strategy on the three channel models and give certain evaluations and suggestion which lay a solid foundation for link and system-level simulations for 5G transmission algorithms.
- 5G,
- Channel model,
- Small-scale parameter,
- Modeling strategy,
- Channel simulation

FullText(HTML)

References(32)

References

[1]	Y. Liu, C. Wang, C. F. Lopez, et al., “3D non-stationary wideband tunnel channel models for 5G high-speed train wireless communications,” IEEE Trans. Intell. Transp. Syst., vol.21, no.1, pp.259–272, 2020. doi: 10.1109/TITS.2019.2890992
[2]	T. Zhou, H. Li, Y. Wang, et al., “Channel modeling for future high-speed railway communication systems: An survey,” IEEE Access, vol.7, pp.52818–52826, 2019. doi: 10.1109/ACCESS.2019.2912408
[3]	A. Ghosh, A. Maeder, M. Baker, et al., “5G evolution: An view on 5G cellular technology beyond 3GPP release 15,” IEEE Access, vol.7, pp.127639–127651, 2019. doi: 10.1109/ACCESS.2019.2939938
[4]	A. Maltsev, A. Pudeyev, I. Bolotin, et al., “Channel modeling and characterization,” V1.0, Document, FP7-ICT-608637/D5.1, MiWEBA, Breitengussbach, Germany, 2014.
[5]	V. Nurmela, A. Karttunen, A. Roivainen, et al., “METIS channel models,” Document, FP7-ICT-317669-METIS/D1.4, Mobile and Wireless Communications Enablers for the Twenty-twenty Information Society (METIS), available online: https://metis2020.com/documents/deliverables, 2015.
[6]	I. Tan, W. Tang, K. Laberteaux, et al., “Measurement and analysis of wireless channel impairments in DSRC vehicular communications,” in Proc. IEEE Int. Conf. Commun., Beijing, pp.4882–4888, 2008.
[7]	O. Renaudin, V. Kolmonen, P. Vainikainen, et al., “Wideband measurement-based modeling of inter-vehicle channels in the 5-GHz band,” IEEE Trans. Veh. Technol., vol.62, no.8, pp.3531–3540, 2013. doi: 10.1109/TVT.2013.2257905
[8]	C. Wang, J. Bian, J. Sun, et al., “A survey of 5G channel measurements and models,” IEEE Commun. Surv. Tut., vol.20, no.4, pp.3142–3168, 2018. doi: 10.1109/COMST.2018.2862141
[9]	X. Zhao, F. Du, S. Geng, et al., “Playback of 5G and beyond measured MIMO channels by an ANN-Based modeling and simulation framework,” IEEE J. Sel. Areas Commun., vol.38, no.9, pp.1945–1954, 2020. doi: 10.1109/JSAC.2020.3000827
[10]	ETSI, “Study on channel model for frequencies from 0.5 to 100 GH,” Tech. Rep., 3GPP TR 38.901 version 15.0.0 Release 15, ETSI TR 138 901 V15.0.0 (2018-07), the 3rd Generation Partnership Project (3GPP), 2018.
[11]	F. Ademaj, M. Taranetz and M. Rupp, “3GPP 3D MIMO channel model: A holistic implementation guideline for open source simulation tools,” EURASIP J. Wireless Com. Netw., vol.2016, article no.55, 2016.
[12]	S. Jaeckel, L. Raschkowski, L. Thiele, et al., “QuaDRiGa− Quasi deterministic radio channel generator, user manual and documentation,” Tech. Rep., v2.2.0, Fraunhofer Heinrich Hertz Institute, 2019.
[13]	J. Flordelis, X. Li, O. Edfors, et al., “Massive MIMO extensions to the COST 2100 channel model: Modeling and validation,” IEEE Transactions on Wireless Communications, vol.19, no.1, pp.380–394, 2020. doi: 10.1109/TWC.2019.2945531
[14]	M. Peter, K. Sakaguchi, S. Jaeckel, et al., “Measurement campaigns and initial channel models for preferred suitable frequency ranges,” Document, ICT-671650-mmMAGIC/D2.1, available online: https://5g-mmmagic.eu/results/, 2016.
[15]	“5G channel model for bands up to 100 GHz, v2.0,” White Paper, Aalto Univ., Espoo, Finland, 2016.
[16]	S. Ju, O. Kanhere, Y. Xing, et al., “A millimeter-wave channel simulator NYUSIM with spatial consistency and human blockage,” 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, pp.1–6, 2019.
[17]	S. Wu, C. Wang, e. M. Aggoune, et al., “A general 3-D non-stationary 5G wireless channel model,” IEEE Trans. Commun., vol.66, no.7, pp.3065–3078, 2018. doi: 10.1109/TCOMM.2017.2779128
[18]	M. Alexander, P. Andrey, K. Ingolf, et al., “Quasi-deterministic approach to mmWave channel modeling in a non-stationary environment,” 2014 IEEE Globecom Workshops (GC Wkshps), Austin, TX, pp.966–971, 2014.
[19]	A. Maltsev, “Channel models for IEEE 802.11ay,” Document, 802.11-15/1150r9, IEEE, New York, NY, USA, 2016.
[20]	Y. He, Y. Zhang, J. Zhang, et al., “Investigation and comparison of QuaDRiGa, NYUSIM and MG5G channel models for 5G wireless communications,” in Proc. IEEE Veh. Technol. Conf., Victoria, pp.1–5, 2020.
[21]	T. S. Rappaport, G. R. MacCartney, M. K. Samimi, et al., “Wideband millimeter-wave propagation measurements and channel models for future wireless communication system design,” IEEE Transactions on Communications, vol.63, no.9, pp.3029–3056, 2015. doi: 10.1109/TCOMM.2015.2434384
[22]	M. K. Samimi and T. S. Rappaport, “3-D millimeter-wave statistical channel model for 5G wireless system design,” IEEE Trans. Micro. Theory and Techniques, vol.64, no.7, pp.2207–2225, 2016. doi: 10.1109/TMTT.2016.2574851
[23]	S. Sun, G. R. MacCartney, M. K. Samimi, et al., “Synthesizing omnidirectional antenna patterns, received power and path loss from directional antennas for 5G millimeter-wave communications,” 2015 IEEE Global Communications Conference (GLOBECOM), San Diego, CA, pp.1–7, 2015.
[24]	T. S. Rappaport, Y. Xing, O. Kanhere, et al., “Wireless communications and applications above 100 GHz: Opportunities and challenges for 6G and beyond,” IEEE Access, vol.7, pp.78729–78757, 2019. doi: 10.1109/ACCESS.2019.2921522
[25]	Y. Xing and T. S. Rappaport, “Propagation measurement system and approach at 140 GHz-moving to 6G and above 100 GHz,” 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, pp.1–6, 2018.
[26]	3GPP Organizational Partners (ARIB, ATIS, CCSA, ETSI, TTA, TTC), “Physical channels and modulation,” Tech. Specif., 3GPP TS 36.211 V12.1.0, 2014.
[27]	A. Adhikary, E. Safadi, M. Samimi, et al., “Joint spatial division and multiplexing for mm-wave channels,” IEEE J. Sel. Areas Commun., vol.32, no.6, pp.1239–1255, 2014. doi: 10.1109/JSAC.2014.2328173
[28]	T. S. Rappaport and S. Deng, “73 GHz wideband millimeter-wave foliage and ground reflection measurements and models,” 2015 IEEE International Conference on Communication Workshop, London, pp.1238–1243, 2015.
[29]	C. Wang, J. Huang, H. Wang, et al., “6G Wireless channel measurements and models: Trends and challenges,” IEEE Veh. Technol. Mag., vol.15, no.4, pp.22–32, 2020. doi: 10.1109/MVT.2020.3018436
[30]	X. You, C. Wang, J. Huang, et al., “Towards 6G wireless communication networks: Vision, enabling technologies, and new paradigm shifts,” Sci. China, Inf. Sci., vol.64, DOI: 10.1007/s11432-020-2955-6, 2021.
[31]	H. Jiang, M. Mukherjee, J. Zhou, et al., “Channel modeling and characteristics for 6G wireless communications,” IEEE Network, vol.35, no.1, pp.296–303, 2021. doi: 10.1109/MNET.011.2000348
[32]	W. Saad, M. Bennis, and M. Chen, “A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,” IEEE Network, vol.34, no.3, pp.134–142, 2020. doi: 10.1109/MNET.001.1900287