Advertisement

A novel 3D GBSM for mmWave MIMO channels

  • Jie Huang
  • Cheng-Xiang WangEmail author
  • Yu Liu
  • Jian Sun
  • Wensheng Zhang
Research Paper
  • 75 Downloads

Abstract

In this paper, a novel three dimensional (3D) wideband geometry-based stochastic model (GBSM) for millimeter wave (mmWave) multiple-input multiple-output (MIMO) channels is proposed. A homogeneous Poisson point process (PPP) is used to generate the clusters in 3D space. The transmitter (Tx) and receiver (Rx) are surrounded by two spheres. The scatterers distributed in the two spheres are introduced to mimic the clustering effects of multipath components (MPCs) in delay and angular domains. The large-scale path loss model and line-of-sight (LOS) probability model are taken into account to make the channel model realistic. In addition, mmWave channel measurements are conducted in an indoor environment. Simulation results based on the two-sphere channel model are compared with measurement results and good agreements are achieved, which validates the proposed channel model. The results indicate that the proposed channel model has good adaptivity and can model the mmWave channel accurately.

Keywords

3D GBSM mmWave channels homogeneous PPP LOS probability channel measurements 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 61771293), Taishan Scholar Program of Shandong Province, EU H2020 ITN 5G Wireless Project (Grant No. 641985), EU FP7 QUICK Project (Grant No. PIRSES-GA-2013-612652), and EU H2020 RISE TESTBED Project (Grant No. 734325).

References

  1. 1.
    Rappaport T S, Sun S, Mayzus R, et al. Millimeter wave mobile communications for 5G cellular: it will work! IEEE Access, 2013, 1: 335–349CrossRefGoogle Scholar
  2. 2.
    Wang C X, Haider F, Gao X, et al. Cellular architecture and key technologies for 5G wireless communication networks. IEEE Commun Mag, 2014, 52: 122–130CrossRefGoogle Scholar
  3. 3.
    Boccardi F, Heath R W, Lozano A, et al. Five disruptive technology directions for 5G. IEEE Commun Mag, 2014, 52: 74–80CrossRefGoogle Scholar
  4. 4.
    Wang C X, Wu S B, Bai L, et al. Recent advances and future challenges for massive MIMO channel measurements and models. Sci China Inf Sci, 2016, 59: 021301Google Scholar
  5. 5.
    Ge X H, Tu S, Mao G Q, et al. 5G ultra-dense cellular networks. IEEE Wirel Commun, 2016, 23: 72–79CrossRefGoogle Scholar
  6. 6.
    Feng R, Huang J, Sun J, et al. A novel 3D frequency domain SAGE algorithm with applications to parameter estimation in mmWave massive MIMO indoor channels. Sci China Inf Sci, 2017, 60: 080305CrossRefGoogle Scholar
  7. 7.
    Yong S K. TG3c channel modeling sub-committee final report. IEEE Standard 802.15-07-0584-01-003c. 2007Google Scholar
  8. 8.
    Maltsev A. Channel models for 60 GHz WLAN systems. IEEE Standard 802.11-09/0334r8. 2010Google Scholar
  9. 9.
    Maltsev A. Channel models for IEEE 802.11ay. IEEE Standard 802.11-15/1150r9. 2016Google Scholar
  10. 10.
    Saleh A A M, Valenzuela R A. A statistical model for indoor multipath propagation. IEEE J Sel Areas Commun, 1987, 5: 128–137CrossRefGoogle Scholar
  11. 11.
    Spencer Q H, Jeffs B D, Jensen M A, et al. Modeling the statistical time and angle of arrival characteristics of an indoor multipath channel. IEEE J Sel Areas Commun, 2000, 18: 347–360CrossRefGoogle Scholar
  12. 12.
    Maltsev A, Pudeyev A, Karls I, et al. Quasi-deterministic approach to mmWave channel modeling in a non-stationary environment. In: Proceedings of IEEE Globecom Workshops, Austin, 2014. 966–971Google Scholar
  13. 13.
    Weiler R J, Peter M, Keusgen W, et al. Quasi-deterministic millimeter-wave channel models in MiWEBA. J Wirel Com Netw, 2016, 84: 1–16Google Scholar
  14. 14.
    Kyösti P. WINNER II Channel Models. Hoboken: John Wiley & Sons, 2008Google Scholar
  15. 15.
    Jaeckel S, Raschkowski L, Börner K, et al. QuaDRiGa: a 3-D multi-cell channel model with time evolution for enabling virtual field trials. IEEE Trans Antenn Propag, 2014, 62: 3242–3256CrossRefGoogle Scholar
  16. 16.
    Wu S B, Wang C X, Aggoune H M, et al. A non-stationary 3-D wideband twin-cluster model for 5G massive MIMO channels. IEEE J Sel Areas Commun, 2014, 32: 1207–1218CrossRefGoogle Scholar
  17. 17.
    Wu S B, Wang C X, Haas H, et al. A non-stationary wideband channel model for massive MIMO communication systems. IEEE Trans Wirel Commun, 2015, 14: 1434–1446CrossRefGoogle Scholar
  18. 18.
    Wu S B, Wang C X, Aggoune H M, et al. A general 3D non-stationary 5G wireless channel model. IEEE Trans Commun, 2018. doi: 10.1109/TCOMM.2017.2779128Google Scholar
  19. 19.
    Wang C X, Bian J, Sun J, et al. A survey of 5G channel measurements and models. IEEE Commun Surv Tut, 2018. in pressGoogle Scholar
  20. 20.
    Bian J, Sun J, Wang C X, et al. A WINNER+ based 3-D non-stationary wideband MIMO channel model. IEEE Trans Wirel Commun, 2018, 17: 1755–1767CrossRefGoogle Scholar
  21. 21.
    Yuan Y, Wang C X, Cheng X, et al. Novel 3D geometry-based stochastic models for non-isotropic MIMO vehicle-tovehicle channels. IEEE Trans Wirel Commun, 2014, 13: 298–309CrossRefGoogle Scholar
  22. 22.
    Yuan Y, Wang C X, He Y J, et al. 3D wideband non-stationary geometry-based stochastic models for non-isotropic MIMO vehicle-to-vehicle channels. IEEE Trans Wirel Commun, 2015, 14: 6883–6895CrossRefGoogle Scholar
  23. 23.
    Ghazal A, Wang C X, Ai B, et al. A nonstationary wideband MIMO channel model for high-mobility intelligent transportation systems. IEEE Trans Intel Transport Syst, 2014, 16: 885–897Google Scholar
  24. 24.
    Ghazal A, Yuan Y, Wang C X, et al. A non-stationary IMT-advanced MIMO channel model for high-mobility wireless communication systems. IEEE Trans Wirel Commun, 2017, 16: 2057–2068CrossRefGoogle Scholar
  25. 25.
    Liu Y, Wang C X, Lopez C F, et al. 3D non-stationary wideband circular tunnel channel models for high-speed train wireless communication systems. Sci China Inf Sci, 2017, 60: 082304CrossRefGoogle Scholar
  26. 26.
    Zeng L Z, Cheng X, Wang C X, et al. A 3D geometry-based stochastic channel model for UAV-MIMO channels. In: Proceedings of IEEE Wireless Communications and Networking Conference, San Francisco, 2017Google Scholar
  27. 27.
    Jiang H, Zhang Z C, Wu L, et al. Novel 3D irregular-shaped geometry-based channel modeling for semi-ellipsoid vehicle-to-vehicle scattering environments. IEEE Wirel Commun Lett, 2018. doi: 10.1109/LWC.2018.2829892Google Scholar
  28. 28.
    Gustafson C, Haneda K, Wyne S, et al. On mm-wave multipath clustering and channel modeling. IEEE Trans Antenn Propag, 2014, 62: 1445–1455CrossRefGoogle Scholar
  29. 29.
    Haneda K, Järveläinen J, Karttunen A, et al. A statistical spatio-temporal radio channel model for large indoor environments at 60 and 70 GHz. IEEE Trans Antenn Propag, 2015, 63: 2694–2704MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Rappaport T S, MacCartney G R, Samimi M K, et al. Wideband millimeter-wave propagation measurements and channel models for future wireless communication system design. IEEE Trans Commun, 2015, 63: 3029–3056CrossRefGoogle Scholar
  31. 31.
    Ko J, Cho Y J, Hur S, et al. Millimeter-wave channel measurements and analysis for statistical spatial channel model in in-building and urban environments at 28 GHz. IEEE Trans Wirel Commun, 2017, 16: 5853–5868CrossRefGoogle Scholar
  32. 32.
    Bai T Y, Heath R W. Analyzing uplink SINR and rate in massive MIMO systems using stochastic geometry. IEEE Trans Commun, 2016, 64: 4592–4606CrossRefGoogle Scholar
  33. 33.
    Andrews J G, Bai T Y, Kulkarni M N, et al. Modeling and analyzing millimeter wave cellular systems. IEEE Trans Commun, 2016, 65: 403–430Google Scholar
  34. 34.
    Alkhateeb A, Nam Y H, Rahman M S, et al. Initial beam association in millimeter wave cellular systems: analysis and design insights. IEEE Trans Wirel Commun, 2017, 16: 2807–2821CrossRefGoogle Scholar
  35. 35.
    MacCartney G R, Samimi M K, Rappaport T S. Omnidirectional path loss models in New York City at 28 GHz and 73 GHz. In: Proceedings of IEEE Annual International Symposium on Personal, Indoor, and Mobile Radio Communication, Washington, 2014. 227–231Google Scholar
  36. 36.
    Järveläinen J, Nguyen S L H, Haneda K, et al. Evaluation of millimeter-wave line-of-sight probability with point cloud data. IEEE Wirel Commun Lett, 2016, 5: 228–231CrossRefGoogle Scholar
  37. 37.
    Pedersen T, Steinböck G, Fleury B H. Modeling of reverberant radio channels using propagation graphs. IEEE Trans Antenn Propag, 2012, 60: 5978–5988MathSciNetCrossRefzbMATHGoogle Scholar
  38. 38.
    Wu X Y, Wang C X, Sun J, et al. 60-GHz millimeter-wave channel measurements and modeling for indoor office environments. IEEE Trans Antenn Propag, 2017, 65: 1912–1924MathSciNetCrossRefGoogle Scholar
  39. 39.
    Huang J, Wang C X, Feng R, et al. Multi-frequency mmWave massive MIMO channel measurements and characterization for 5G wireless communication systems. IEEE J Sel Areas Commun, 2017, 35: 1591–1605CrossRefGoogle Scholar
  40. 40.
    Zhang B, Zhong Z, Zhou X, et al. Path loss characteristics of indoor radio channels at 15 GHz. In: Proceedings of European Conference on Antennas and Propagation, Davos, 2016. 1–5Google Scholar
  41. 41.
    Huang J, Feng R, Sun J, et al. Comparison of propagation channel characteristics for multiple millimeter wave bands. In: Proceedings of IEEE Vehicular Technology Conference, Sydney, 2017. 1–5Google Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Jie Huang
    • 1
  • Cheng-Xiang Wang
    • 1
    • 2
    Email author
  • Yu Liu
    • 1
  • Jian Sun
    • 1
  • Wensheng Zhang
    • 1
  1. 1.Shandong Provincial Key Lab of Wireless Communication Technologies, School of Information Science and EngineeringShandong UniversityQingdaoChina
  2. 2.Institute of Sensors, Signals and Systems, School of Engineering & Physical SciencesHeriot-Watt UniversityEdinburghUK

Personalised recommendations