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Indoor massive multiple-input multiple-output channel characterization and performance evaluation

  • Jian-zhi Li
  • Bo Ai
  • Rui-si He
  • Qi Wang
  • Mi Yang
  • Bei Zhang
  • Ke Guan
  • Dan-ping He
  • Zhang-dui Zhong
  • Ting Zhou
  • Nan Li
Article

Abstract

We present a measurement campaign to characterize an indoor massive multiple-input multiple-output (MIMO) channel system, using a 64-element virtual linear array, a 64-element virtual planar array, and a 128-element virtual planar array. The array topologies are generated using a 3D mechanical turntable. The measurements are conducted at 2, 4, 6, 11, 15, and 22 GHz, with a large bandwidth of 200 MHz. Both line-of-sight (LOS) and non-LOS (NLOS) propagation scenarios are considered. The typical channel parameters are extracted, including path loss, shadow fading, power delay profile, and root mean square (RMS) delay spread. The frequency dependence of these channel parameters is analyzed. The correlation between shadow fading and RMS delay spread is discussed. In addition, the performance of the standard linear precoder—the matched filter, which can be used for intersymbol interference (ISI) mitigation by shortening the RMS delay spread, is investigated. Other performance measures, such as entropy capacity, Demmel condition number, and channel ellipticity, are analyzed. The measured channels, which are in a rich-scattering indoor environment, are found to achieve a performance close to that in independent and identically distributed Rayleigh channels even in an LOS scenario.

Key words

Massive MIMO Channel modeling 5G Shadow fading Delay spread Matched filter Entropy capacity Condition number Channel ellipticity 

CLC number

TN92 

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References

  1. Ai, B., Cheng, X., Kürner, T., et al., 2014. Challenges toward wireless communications for high-speed railway. IEEE Trans. Intell. Transp. Syst., 15(5): 2143–2158. http://dx.doi.org/10.1109/TITS.2014.2310771CrossRefGoogle Scholar
  2. Ai, B., Guan, K., Rupp, M., et al., 2015. Future railway services-oriented mobile communications network. IEEE Commun. Mag., 53(10): 78–85. http://dx.doi.org/10.1109/MCOM.2015.7295467CrossRefGoogle Scholar
  3. Ai, B., Guan, K., He, R.S., et al., 2017. On indoor millimeter wave massive MIMO channels: measurement and simulation. IEEE J. Sel. Areas Commun., 99: 1–17. http://dx.doi.org/10.1109/JSAC.2017.2698780Google Scholar
  4. Andrews, J.G., Buzzi, S., Choi, W., et al., 2014. What will 5G be? IEEE J. Sel. Areas Commun., 32(6): 1065–1082. http://dx.doi.org/10.1109/JSAC.2014.2328098CrossRefGoogle Scholar
  5. Astely, D., Dahlman, E., Furuskär, A., et al., 2009. LTE: the evolution of mobile broadband. IEEE Commun. Mag., 47(4): 44–51. http://dx.doi.org/10.1109/MCOM.2009.4907406CrossRefGoogle Scholar
  6. Boccardi, F., Heath, R.W., Lozano, A., et al., 2014. Five disruptive technology directions for 5G. IEEE Commun. Mag., 52(2): 74–80. http://dx.doi.org/10.1109/MCOM.2014.6736746CrossRefGoogle Scholar
  7. Cai, Y., de Lamare, R.C., Champagne, B., et al., 2015. Adaptive reduced-rank receive processing based on minimum symbol-error-rate criterion for large-scale multiple-antenna systems. IEEE Trans. Commun., 63(11): 4185–4201. http://dx.doi.org/10.1109/TCOMM.2015.2475260CrossRefGoogle Scholar
  8. Demmel, J.W., 1988. The probability that a numerical analysis problem is difficult. Math. Comput., 50(182): 449–480. http://dx.doi.org/10.1090/S0025-5718-1988-0929546-7MathSciNetCrossRefMATHGoogle Scholar
  9. Feuerstein, M.J., Blackard, K.L., Rappaport, T.S., et al., 1994. Path loss, delay spread, and outage models as functions of antenna height for microcellular system design. IEEE Trans. Veh. Technol., 43(3): 487–498. http://dx.doi.org/10.1109/25.312809CrossRefGoogle Scholar
  10. Flordelis, J., Gao, X., Dahman, G., et al., 2015. Spatial separation of closely-spaced users in measured massive multi-user MIMO channels. IEEE Int. Conf. on Communications, p.1441–1446. http://dx.doi.org/10.1109/ICC.2015.7248526Google Scholar
  11. Gao, L., Zhong, Z., Ai, B., et al., 2010. Estimation of the Ricean K factor in the high speed railway scenarios. 5th Int. Conf. on Communications and Networking in China, p.1–5.Google Scholar
  12. Gao, X., Tufvesson, F., Edfors, O., et al., 2012. Measured propagation characteristics for very-large MIMO at 2.6 GHz. 46th Asilomar Conf. on Signals, Systems and Computers, p.295–299. http://dx.doi.org/10.1109/ACSSC.2012.6489010Google Scholar
  13. Gao, X., Edfors, O., Rusek, F., et al., 2015. Massive MIMO performance evaluation based on measured propagation data. IEEE Trans. Wirel. Commun., 14(7): 3899–3911. http://dx.doi.org/10.1109/TWC.2015.2414413CrossRefGoogle Scholar
  14. Greenstein, L.J., Erceg, V., Yeh, Y.S., et al., 1997. A new path-gain/delay-spread propagation model for digital cellular channels. IEEE Trans. Veh. Technol., 46(2): 477–485. http://dx.doi.org/10.1109/25.580786CrossRefGoogle Scholar
  15. Guan, K., Zhong, Z., Ai, B., et al., 2013a. Deterministic propagation modeling for the realistic high-speed railway environment. IEEE 77th Vehicular Technology Conf., p.1–5. http://dx.doi.org/10.1109/VTCSpring.2013.6692506Google Scholar
  16. Guan, K., Zhong, Z., Ai, B., et al., 2013b. Modeling of the division point of different propagation mechanisms in the near-region within arched tunnels. Wirel. Pers. Commun., 68(3): 489–505. http://dx.doi.org/10.1007/s11277-011-0464-7CrossRefGoogle Scholar
  17. Guan, K., Zhong, Z., Ai, B., et al., 2014a. Propagation measurements and analysis for train stations of highspeed railway at 930 MHz. IEEE Trans. Veh. Technol., 63(8): 3499–3516. http://dx.doi.org/10.1109/TVT.2014.2307917CrossRefGoogle Scholar
  18. Guan, K., Zhong, Z., Ai, B., et al., 2014b. Propagation measurements and modeling of crossing bridges on highspeed railway at 930 MHz. IEEE Trans. Veh. Technol., 63(2): 502–517. http://dx.doi.org/10.1109/TVT.2013.2275912CrossRefGoogle Scholar
  19. Guan, K., Ai, B., Nicolás, M.L., et al., 2016. On the influence of scattering from traffic signs in vehicle-to-x communications. IEEE Trans. Veh. Technol., 65(8): 5835–5849. http://dx.doi.org/10.1109/TVT.2015.2476335CrossRefGoogle Scholar
  20. He, R., Zhong, Z., Ai, B., et al., 2011. An empirical path loss model and fading analysis for high-speed railway viaduct scenarios. IEEE Antennas Wirel. Propag. Lett., 10: 808–812. http://dx.doi.org/10.1109/LAWP.2011.2164389CrossRefGoogle Scholar
  21. He, R., Zhong, Z., Ai, B., et al., 2012a. Analysis of the relation between Fresnel zone and path loss exponent based on two-ray model. IEEE Antennas Wirel. Propag. Lett., 11: 208–211. http://dx.doi.org/10.1109/LAWP.2012.2187270CrossRefGoogle Scholar
  22. He, R., Zhong, Z., Ai, B., et al., 2012b. Measurements and analysis of short-term fading behavior for highspeed rail viaduct scenario. IEEE Int. Conf. on Communications, p.4563–4567. http://dx.doi.org/10.1109/ICC.2012.6363678Google Scholar
  23. He, R., Zhong, Z., Ai, B., et al., 2013. Measurements and analysis of propagation channels in high-speed railway viaducts. IEEE Trans. Wirel. Commun., 12(2): 794–805. http://dx.doi.org/10.1109/TWC.2012.120412.120268CrossRefGoogle Scholar
  24. He, R., Molisch, A.F., Tufvesson, F., et al., 2014. Vehicle-tovehicle propagation models with large vehicle obstructions. IEEE Trans. Intell. Transp. Syst., 15(5): 2237–2248. http://dx.doi.org/10.1109/TITS.2014.2311514CrossRefGoogle Scholar
  25. He, R., Renaudin, O., Kolmonen, V.M., et al., 2015a. Characterization of quasi-stationarity regions for vehicle-tovehicle radio channels. IEEE Trans. Antennas Propag., 63(5): 2237–2251. http://dx.doi.org/10.1109/TAP.2015.2402291CrossRefGoogle Scholar
  26. He, R., Zhong, Z., Ai, B., et al., 2015b. Shadow fading correlation in high-speed railway environments. IEEE Trans. Veh. Technol., 64(7): 2762–2772. http://dx.doi.org/10.1109/TVT.2014.2351579Google Scholar
  27. He, R., Ai, B., Wang, G., et al., 2016a. High-speed railway communications: from GSM-R to LTE-R. IEEE Veh. Technol. Mag., 11(3): 49–58. http://dx.doi.org/10.1109/MVT.2016.2564446CrossRefGoogle Scholar
  28. He, R., Chen, W., Ai, B., et al., 2016b. On the clustering of radio channel impulse responses using sparsity-based methods. IEEE Trans. Antennas Propag., 64(6): 2465–2474. http://dx.doi.org/10.1109/TAP.2016.2546953MathSciNetCrossRefGoogle Scholar
  29. Heath, R.W., Paulraj, A.J., 2005. Switching between diversity and multiplexing in MIMO systems. IEEE Trans. Commun., 53(6): 962–968. http://dx.doi.org/10.1109/TCOMM.2005.849774CrossRefGoogle Scholar
  30. Hoydis, J., Hoek, C., Wild, T., et al., 2012. Channel measurements for large antenna arrays. Int. Symp. on Wireless Communication Systems, p.811–815. http://dx.doi.org/10.1109/ISWCS.2012.6328480Google Scholar
  31. Janssen, G.J.M., Stigter, P.A., Prasad, R., 1996. Wideband indoor channel measurements and BER analysis of frequency selective multipath channels at 2.4, 4.75, and 11.5 GHz. IEEE Trans. Commun., 44(10): 1272–1288. http://dx.doi.org/10.1109/26.539768CrossRefGoogle Scholar
  32. Jungnickel, V., Jaeckel, S., Thiele, L., et al., 2009. Capacity measurements in a cooperative MIMO network. IEEE Trans. Veh. Technol., 58(5): 2392–2405. http://dx.doi.org/10.1109/TVT.2008.2010260CrossRefGoogle Scholar
  33. Larsson, E.G., Edfors, O., Tufvesson, F., et al., 2014. Massive MIMO for next generation wireless systems. IEEE Commun. Mag., 52(2): 186–195. http://dx.doi.org/10.1109/MCOM.2014.6736761CrossRefGoogle Scholar
  34. Li, J., Ai, B., He, R., et al., 2016. Measurement-based characterizations of indoor massive MIMO channels at 2 GHz, 4 GHz, and 6 GHz frequency bands. IEEE 83rd Vehicular Technology Conf., p.1–5. http://dx.doi.org/10.1109/VTCSpring.2016.7504341Google Scholar
  35. Liu, L., Li, Y., Zhang, J., 2014. DoA estimation and achievable rate analysis for 3D millimeter wave massive MIMO systems. IEEE 15th Int. Workshop on Signal Processing Advances in Wireless Communications, p.6–10. http://dx.doi.org/10.1109/SPAWC.2014.6941306Google Scholar
  36. Molisch, A.F., 2011. Wireless Communications. Wiley-IEEE Press, Hoboken, USA.Google Scholar
  37. Molisch, A.F., Steinbauer, M., 1999. Condensed parameters for characterizing wideband mobile radio channels. Int. J. Wirel. Inform. Netw., 6(3): 133–154. http://dx.doi.org/10.1023/A:1018895720076CrossRefGoogle Scholar
  38. Ng, B.L., Kim, Y., Lee, J., et al., 2012. Fulfilling the promise of massive MIMO with 2D active antenna array. IEEE Globecom Workshops, p.691–696. http://dx.doi.org/10.1109/GLOCOMW.2012.6477658Google Scholar
  39. Ngo, H.Q., Larsson, E.G., Marzetta, T.L., 2013. Energy and spectral efficiency of very large multiuser MIMO systems. IEEE Trans. Commun., 61(4): 1436–1449. http://dx.doi.org/10.1109/TCOMM.2013.020413.110848CrossRefGoogle Scholar
  40. Payami, S., Tufvesson, F., 2012. Channel measurements and analysis for very large array systems at 2.6 GHz. 6th European Conf. on Antennas and Propagation, p.433–437. http://dx.doi.org/10.1109/EuCAP.2012.6206345Google Scholar
  41. Payami, S., Tufvesson, F., 2013. Delay spread properties in a measured massive MIMO system at 2.6 GHz. IEEE 24th Annual Int. Symp. on Personal, Indoor, and Mobile Radio Communications, p.53–57. http://dx.doi.org/10.1109/PIMRC.2013.6666103Google Scholar
  42. Poon, A.S.Y., Ho, M., 2003. Indoor multiple-antenna channel characterization from 2 to 8 GHz. IEEE Int. Conf. on Communications, p.3519–3523. http://dx.doi.org/10.1109/ICC.2003.1204108Google Scholar
  43. Rusek, F., Persson, D., Lau, B.K., et al., 2013. Scaling up MIMO: opportunities and challenges with very large arrays. IEEE Signal Process. Mag., 30(1): 40–60. http://dx.doi.org/10.1109/MSP.2011.2178495CrossRefGoogle Scholar
  44. Salo, J., Suvikunnas, P., El-Sallabi, H.M., et al., 2006. Ellipticity statistic as measure of MIMO multipath richness. Electron. Lett., 42(3): 160–162. http://dx.doi.org/10.1049/el:20063847CrossRefGoogle Scholar
  45. Salous, S., Gokalp, H., 2007. Medium-and large-scale characterization of UMTS-allocated frequency division duplex channels. IEEE Trans. Veh. Technol., 56(5): 2831–2843. http://dx.doi.org/10.1109/TVT.2007.900495CrossRefGoogle Scholar
  46. Wang, C.X., Haider, F., Gao, X., et al., 2014. Cellular architecture and key technologies for 5G wireless communication networks. IEEE Commun. Mag., 52(2): 122–130. http://dx.doi.org/10.1109/MCOM.2014.6736752CrossRefGoogle Scholar
  47. Wei, H., Zhong, Z., Xiong, L., et al., 2011. Study on the shadow fading characteristic in viaduct scenario of the high-speed railway. 6th Int. Conf. on Communications and Networking in China, p.1216–1220. http://dx.doi.org/10.1109/ChinaCom.2011.6158343Google Scholar
  48. Wu, S., Wang, C.X., Haas, H., et al., 2015. A non-stationary wideband channel model for massive MIMO communication systems. IEEE Trans. Wirel. Commun., 14(3): 1434–1446. http://dx.doi.org/10.1109/TWC.2014.2366153CrossRefGoogle Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Jian-zhi Li
    • 1
  • Bo Ai
    • 1
    • 2
  • Rui-si He
    • 1
  • Qi Wang
    • 1
  • Mi Yang
    • 1
  • Bei Zhang
    • 1
  • Ke Guan
    • 1
  • Dan-ping He
    • 1
  • Zhang-dui Zhong
    • 1
  • Ting Zhou
    • 3
  • Nan Li
    • 4
  1. 1.State Key Laboratory of Rail Traffic Control and SafetyBeijing Jiaotong UniversityBeijingChina
  2. 2.Engineering University of Armed Police ForceTelecommunication Engineering DepartmentXi’anChina
  3. 3.Key Laboratory of Wireless Sensor Network and Communication, Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghaiChina
  4. 4.ZTE CorporationBeijingChina

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