Channel measurements and models for 6G: current status and future outlook

Abstract

With the commercialization of fifth generation networks worldwide, research into sixth generation (6G) networks has been launched to meet the demands for high data rates and low latency for future services. A wireless propagation channel is the transmission medium to transfer information between the transmitter and the receiver. Moreover, channel properties determine the ultimate performance limit of wireless communication systems. Thus, conducting channel research is a prerequisite to designing 6G wireless communication systems. In this paper, we first introduce several emerging technologies and applications for 6G, such as terahertz communication, industrial Internet of Things, space-air-ground integrated network, and machine learning, and point out the developing trends of 6G channel models. Then, we give a review of channel measurements and models for the technologies and applications. Finally, the outlook for 6G channel measurements and models is discussed.

This is a preview of subscription content, access via your institution.

References

  1. 3GPP, 2018. Study on Channel Model for Frequencies from 0.5 to 100 GHz. Technical Report TR 38.901, 3GPP.

  2. 3GPP, 2019. Study on Evaluation Methodology of New Vehicle-to-Everything (V2X) Use Cases for LTE and NR. Technical Report TR 37.885, 3GPP.

  3. 5G-ACIA, 2018. LS on Channel Model for Indoor Industrial Scenarios. Proposal RP-181521, 5G-ACIA.

  4. Ai Y, Cheffena M, Li Q, 2015. Radio frequency measurements and capacity analysis for industrial indoor environments. Proc 9th European Conf on Antennas and Propagation, p.1–5.

  5. Ali E, Ismail M, Nordin R, et al., 2017. Beamforming techniques for massive MIMO systems in 5G: overview, classification, and trends for future research. Front Inform Technol Electron Eng, 18(6):753–772. https://doi.org/10.1631/FITEE.1601817

    Article  Google Scholar 

  6. Almeida JJH, Lopes PB, Akamine C, et al., 2018. An application of neural networks to channel estimation of the ISDB-TB FBMC system. https://arxiv.org/abs/1803.01141

  7. Alpaydin E, 2006. Introduction to Machine Learning. MIT Press, USA. Al-Hourani A, Kandeepan S, Jamalipour A, 2014. Modeling air-to-ground path loss for low altitude platforms in urban environments. IEEE Global Communications Conf, p.2898–2904. https://doi.org/10.1109/GLOCOM.2014.7037248

  8. Al-Saegh AM, Sali A, Mandeep JS, et al., 2017. Channel measurements, characterization, and modeling for land mobile satellite terminals in tropical regions at Kuband. IEEE Trans Veh Technol, 66(2):897–911. https://doi.org/10.1109/TVT.2016.2563038

    Article  Google Scholar 

  9. Arndt D, Ihlow A, Heuberger A, et al., 2011. Antenna diversity for mobile satellite applications: performance evaluation based on measurements. Proc 5th European Conf on Antennas and Propagation, p.3729–3733.

  10. Baum LE, Petrie T, 1966. Statistical inference for probabilistic functions of finite state Markov chains. Ann Math Stat, 37(6):1554–1563.

    MathSciNet  MATH  Article  Google Scholar 

  11. Berardinelli G, Mahmood NH, Rodriguez I, et al., 2018. Beyond 5G wireless IRT for Industry 4.0: design principles and spectrum aspects. IEEE Globecom Workshops, p.1–6. https://doi.org/10.1109/glocomw.2018.8644245

  12. Bishop CM, 2006. Pattern Recognition and Machine Learning. Springer, New York.

    Google Scholar 

  13. Cerwall P, Jonsson P, Möller R, et al., 2015. Ericsson Mobility Report. Telefonaktiebolaget LM Ericsson, Stockholm, Sweden.

    Google Scholar 

  14. Chen JJ, Yin XF, Cai XS, et al., 2017. Measurement-based massive MIMO channel modeling for outdoor LoS and NLoS environments. IEEE Access, 5:2126–2140. https://doi.org/10.1109/ACCESS.2017.2652983

    Article  Google Scholar 

  15. Chen XB, Tian L, Tang P, et al., 2016. Modelling of human body shadowing based on 28 GHz indoor measurement results. IEEE 84th Vehicular Technology Conf, p.1–5. https://doi.org/10.1109/VTCFall.2016.7881093

  16. Chen XF, Han Z, Zhang HG, et al., 2018. Wireless resource scheduling in virtualized radio access networks using stochastic learning. IEEE Trans Mob Comput, 17(4):961–974. https://doi.org/10.1109/TMC.2017.2742949

    Article  Google Scholar 

  17. Cheng CL, Kim S, Zajić A, 2017. Comparison of path loss models for indoor 30 GHz, 140 GHz, and 300 GHz channels. Proc 11th European Conf on Antennas and Propagation, p.716–720. https://doi.org/10.23919/EuCAP.2017.7928124

  18. Cheng X, Li YR, 2019. A 3-D geometry-based stochastic model for UAV-MIMO wideband nonstationary channels. IEEE Int Things J, 6(2):1654–1662. https://doi.org/10.1109/JIOT.2018.2874816

    Article  Google Scholar 

  19. Cisco, 2019. Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2017–2022 White Paper. Cisco Systems, Inc., CA, USA.

    Google Scholar 

  20. CMCC, BUPT, 2018a. New Measurements and Modelling on Fast Fading in IIOT Scenarios. Proposal RP-1904743, 3GPP.

  21. CMCC, BUPT, 2018b. New Measurements and Modelling on Pathloss in IIOT Scenarios. Proposal RP-1904742, 3GPP.

  22. CMRI, 2019. The Outlook and Demand Report for 2030+. China Mobile Resesrch Institute, Bejing (in Chinese). https://cmri.chinamobile.com/news/5985.html [Accessed on Jan. 4, 2020].

  23. Cortes C, Vapnik V, 1995. Support-vector networks. Mach Learn, 20(3):273–297.

    MATH  Google Scholar 

  24. Dahlman E, Mildh G, Parkvall S, et al., 2014. 5G wireless access: requirements and realization. IEEE Commun Mag, 52(12):42–47. https://doi.org/10.1109/MCOM.2014.6979985

    Article  Google Scholar 

  25. Dai L, Zhang H, Zhuang Y, 2018. Propagation-model-free coverage evaluation via machine learning for future 5G networks. IEEE 29th Annual Int Symp on Personal, Indoor and Mobile Radio Communications, p.1–5. https://doi.org/10.1109/PIMRC.2018.8580992

  26. Darak SJ, Zhang HG, Palicot J, et al., 2017. Decision making policy for RF energy harvesting enabled cognitive radios in decentralized wireless networks. Dig Signal Process, 60:33–45. https://doi.org/10.1016/j.dsp.2016.08.014

    Article  Google Scholar 

  27. Dreyfus SE, 2012. Artificial neural networks, back propagation, and the Kelley-Bryson gradient procedure. J Guid Contr Dynam, 13(5):926–928.

    MathSciNet  Article  Google Scholar 

  28. Ericsson, 2019a. Summary of e]Discussion on Additional Modelling Components. Proposal RP-1905197, 3GPP.

  29. Ericsson, 2019b. Views on Additional Modelling Components. Proposal RP-1905203, 3GPP.

  30. Feng QX, McGeehan J, Tameh EK, et al., 2006. Path loss models for air-to-ground radio channels in urban environments. IEEE 63rd Vehicular Technology Conf, p.2901–2905. https://doi.org/10.1109/VETECS.2006.1683399

  31. Ferrer-Coll J, Ängskog P, Chilo J, et al., 2012. Characterisation of highly absorbent and highly reflective radio wave propagation environments in industrial applications. IET Commun, 6(15):2404–2412. https://doi.org/10.1049/iet-com.2012.0028

    Article  Google Scholar 

  32. Freund Y, Schapire R, Abe N, 1999. A short introduction to boosting. JJpn Soc Artif Intell, 14(5):771–780.

    Google Scholar 

  33. Gao X, Chen Z, Hu Y, 2013. Analysis ofunmanned aerial vehicle MIMO channel capacity based on aircraft attitude. WSEAS Trans Inform Sci Appl, 10(2):58–67.

    Google Scholar 

  34. Giordani M, Polese M, Mezzavilla M, et al., 2019. Towards 6G networks: use cases and technologies. https://arxiv.org/abs/1903.12216

  35. Goddemeier N, Wietfeld C, 2015. Investigation of air-to-air channel characteristics and a UAV specific extension to the rice model. IEEE Globecom Workshops, p.1–5. https://doi.org/10.1109/GLOCOMW.2015.7414180

  36. Goldhirsh J, Vogel W, 1987. Roadside tree attenuation measurements at UHF for land mobile satellite systems. IEEE Trans Antenn Propag, 35(5):589–596. https://doi.org/10.1109/TAP.1987.1144137

    Article  Google Scholar 

  37. Goldsmith A, Jafar SA, Jindal N, et al., 2003. Capacity limits of MIMO channels. IEEE J Sel Areas Commun, 21(5):684–702. https://doi.org/10.1109/JSAC.2003.810294

    Article  Google Scholar 

  38. Haas E, 2002. Aeronautical channel modeling. IEEE Trans Veh Technol, 51(2):254–264. https://doi.org/10.1109/25.994803

    MathSciNet  Article  Google Scholar 

  39. Hanssens B, Kshetri SR, Tanghe E, et al., 2018. Measurement-based analysis of dense multipath components in a large industrial warehouse. 12th European Conf on Antennas and Propagation, p.1–5. https://doi.org/10.1049/cp.2018.0453

  40. Hess GC, 1980. Land-mobile satellite excess path loss measurements. IEEE Trans Veh Technol, 29(2):290–297. https://doi.org/10.1109/T-VT.1980.23854

    Article  Google Scholar 

  41. Hochreiter S, Schmidhuber J, 1997. Long short-term memory. Neur Comput, 9(7):1735–1780.

    Article  Google Scholar 

  42. Holfeld B, Wieruch D, Raschkowski L, et al., 2016. Radio channel characterization at 5.85 GHz for wireless M2M communication of industrial robots. IEEE Wireless Communications and Networking Conf, p.1–7. https://doi.org/10.1109/WCNC.2016.7564890

  43. Hu BB, Nuss MC, 1995. Imaging with terahertz waves. Opt Lett, 20(16):1716–1718. https://doi.org/10.1364/OL.20.001716

    Article  Google Scholar 

  44. Huang C, Huang KW, Wen Y, et al., 2016. A propose of the ISS space-to-space communication system by multiplexing ground mobile communication frequency resources. 6th Int Conf on Instrumentation & Measurement, Computer, Communication and Control, p.567–569. https://doi.org/10.1109/IMCCC.2016.123

  45. Huang HJ, Yang J, Song Y, et al., 2018. Deep learning for super-resolution channel estimation and DOA estimation based massive MIMO system. IEEE Trans Veh Technol, 67(9):8549–8560. https://doi.org/10.1109/TVT.2018.2851783

    Article  Google Scholar 

  46. Huawei, HiSilicon, 2018. Preliminary Channel Measurement on Large-Scale Propagation Loss for Indoor Factory Environment. Proposal RP-1904706, 3GPP.

  47. ITU-R, 2013. Attenuation by Atmospheric Gases. Recommendation P.676–10, ITU-R, Geneva, Switzerland.

    Google Scholar 

  48. ITU-R, 2015. IMT Traffic Estimates for the Years 2020 to 2030. Report M.2370, ITU-R, Geneva, Switzerland.

    Google Scholar 

  49. ITU-T, 2019. Architectural Framework for Machine Learning in Future Networks Including IMT-2020. Recommendation Y.3172, ITU-T, Geneva, Switzerland.

    Google Scholar 

  50. Jacob M, Priebe S, Dickhoff R, et al., 2012. Diffraction in mm and sub-mm wave indoor propagation channels. IEEE Trans Microw Theory Technol, 60(3):833–844. https://doi.org/10.1109/TMTT.2011.2178859

    Article  Google Scholar 

  51. Jansen C, Piesiewicz R, Mittleman D, et al., 2008. The impact of reflections from stratified building materials on the wave propagation in future indoor terahertz communication systems. IEEE Trans Antenn Propag, 56(5):1413–1419. https://doi.org/10.1109/TAP.2008.922651

    Article  Google Scholar 

  52. Jansen C, Priebe S, Moller C, et al., 2011. Diffuse scattering from rough surfaces in THz communication channels. IEEE Trans Terahertz Sci Technol, 1(2):462–472. https://doi.org/10.1109/TTHZ.2011.2153610

    Article  Google Scholar 

  53. Jiang CX, Zhang HJ, Ren Y, et al., 2016. Machine learning paradigms for next-generation wireless networks. IEEE Wirel Commun, 24(2):98–105. https://doi.org/10.1109/MWC.2016.1500356WC

    Article  Google Scholar 

  54. Joo EM, Zhou Y, 2009. Theory and Novel Applications of Machine Learning. IntechOpen, London, UK.

    Google Scholar 

  55. Kalman RE, 1960. A new approach to linear filtering and prediction problems. J Bas Eng, 82(1):35–45.

    MathSciNet  Article  Google Scholar 

  56. Karedal J, Wyne S, Almers P, et al., 2007. A measurement-based statistical model for industrial ultra-wideband channels. IEEE Trans Wirel Commun, 6(8):3028–3037. https://doi.org/10.1109/TWC.2007.051050

    Article  Google Scholar 

  57. Khalid N, Akan OB, 2016. Wideband THz communication channel measurements for 5G indoor wireless networks. IEEE Int Conf on Communications. https://doi.org/10.1109/ICC.2016.7511280

  58. Khawaja W, Guvenc I, Matolak D, 2016. UWB channel sounding and modeling for UAV air-to-ground propagation channels. IEEE Global Communications Conf, p.1–7. https://doi.org/10.1109/GLOCOM.2016.7842372

  59. Khawaja W, Guvenc I, Matolak DW, et al., 2019. A survey of air-to-ground propagation channel modeling for unmanned aerial vehicles. IEEE Commun Surv Tutor, 21(3):2361–2391. https://doi.org/10.1109/COMST.2019.2915069

    Article  Google Scholar 

  60. Kim S, Zajić AG, 2015. Statistical characterization of 300-GHz propagation on a desktop. IEEE Trans Veh Technol, 64(8):3330–3338. https://doi.org/10.1109/TVT.2014.2358191

    Article  Google Scholar 

  61. Lacoste F, Carvalho F, Fontan FP, et al., 2010. MISO and SIMO measurements of the land mobile satellite propagation channel at S-band. Proc 4th European Conf on Antennas and Propagation, p.1–5.

  62. Lacoste F, Lemorton J, Casadebaig L, et al., 2012. Measurements of the land mobile and nomadic satellite channels at 2.2 GHz and 3.8 GHz. 6th European Conf on Antennas and Propagation, p.2422–2426. https://doi.org/10.1109/EuCAP.2012.6206356

  63. Lei MY, Zhang JH, Lei T, et al., 2015. 28-GHz indoor channel measurements and analysis of propagation characteristics. IEEE 25th Annual Int SymponPersonal, Indoor, and Mobile Radio Communication. https://doi.org/10.1109/PIMRC.2014.7136161

  64. Li HH, Li YZ, Zhou SD, et al., 2017. Wireless channel feature extraction via GMM and CNN in the tomographic channel model. J Commun Inform Netw, 2(1):41–51. https://doi.org/10.1007/s41650-017-0004-z

    Article  Google Scholar 

  65. Li JZ, Ai B, He RS, et al., 2017. Indoor massive multiple-input multiple-output channel characterization and performance evaluation. Front Inform Technol Electron Eng, 18(6):773–787. https://doi.org/10.1631/FITEE.1700021

    Article  Google Scholar 

  66. Li W, Zhang JH, Ma XC, et al., 2019. The way to apply machine learning to IoT driven wireless network from channel perspective. China Commun, 16(1):148–164.

    Google Scholar 

  67. Li WZ, Law CL, Dubey VK, et al., 2001. Ka-band land mobile satellite channel model incorporating weather effects. IEEE Commun Lett, 5(5):194–196. https://doi.org/10.1109/4234.922757

    Article  Google Scholar 

  68. Li Y, Zhao L, Wang H, 2012. A novel mobility model for clustered MANET. 8th Int Conf on Wireless Communications, Networking and Mobile Computing, p.1–4. https://doi.org/10.1109/WiCOM.2012.6478340

  69. Li YP, Zhang JH, Ma ZY, et al., 2018. Clustering analysis in the wireless propagation channel with a variational Gaussian mixture model. IEEE Trans Big Data, online. https://doi.org/10.1109/TBDATA.2018.2840696

  70. Lin L, Zhu M, 2018. Efficient tracking of moving target based on an improved fast differential evolution algorithm. IEEE Access, 6:6820–6828. https://doi.org/10.1109/ACCESS.2018.2793298

    Article  Google Scholar 

  71. Liu GY, Hou XY, Wang F, et al., 2016. Achieving 3D-MIMO with massive antennas from theory to practice with evaluation and field trial results. IEEE Syst J, 11(1):62–71. https://doi.org/10.1109/JSYST.2015.2477503

    Article  Google Scholar 

  72. Liu JJ, Shi YP, Fadlullah ZM, et al., 2018. Space-air-ground integrated network: a survey. IEEE Commun Surv Tutor, 20(4):2714–2741. https://doi.org/10.1109/COMST.2018.2841996

    Article  Google Scholar 

  73. Liu L, Zhang K, Tao C, et al., 2018. Channel measurements and characterizations for automobile factory environments. 20th Int Conf on Advanced Communication Technology, p.234–238. https://doi.org/10.23919/ICACT.2018.8323708

  74. Liu XQ, Chen HH, Chen SY, et al., 2017. Symbol cyclic-shift equalization algorithm—a CP-free OFDM/OFDMA system design. IEEE Trans Veh Technol, 66(1):282–294. https://doi.org/10.1109/TVT.2016.2542106

    MathSciNet  Google Scholar 

  75. Loo C, 1996. Statistical models for land mobile and fixed satellite communications at Ka band. Proc Vehicular Technology Conf, p.1023–1027. https://doi.org/10.1109/VETEC.1996.501466

  76. Lu B, Wang CX, Jie H, et al., 2018. Predicting wireless mmwave massive MIMO channel characteristics using machine learning algorithms. Wirel Commun Mob Comput, 2018:9783863. https://doi.org/10.1155/2018/9783863

    Google Scholar 

  77. Luan FY, Zhang Y, Xiao LM, et al., 2013. Fading characteristics of wireless channel on high-speed railway in hilly terrain scenario. Int J Antenn Propag, 2013:378407. https://doi.org/10.1155/2013/378407

    Article  Google Scholar 

  78. Luo SP, Polu N, Chen ZX, et al., 2011. RF channel modeling of a WSN testbed for industrial environment. IEEE Radio and Wireless Symp, p.375–378. https://doi.org/10.1109/RWS.2011.5725435

  79. Lutz E, Cygan D, Dippold M, et al., 1991. The land mobile satellite communication channel-recording, statistics, and channel model. IEEE Trans Veh Technol, 40(2):375–386. https://doi.org/10.1109/25.289418

    Article  Google Scholar 

  80. Ma XC, Zhang JH, Zhang YX, et al., 2017. A PCA-based modeling method for wireless MIMO channel. IEEE Conf on Computer Communications Workshops, p.874–879. https://doi.org/10.1109/infcomw.2017.8116491

  81. Martínez ÀO, de Carvalho E, Nielsen JØ, 2014. Towards very large aperture massive MIMO: a measurement based study. IEEE Globecom Workshops, p.281–286. https://doi.org/10.1109/GLOCOMW.2014.7063445

  82. Marzetta TL, 2010. Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Trans Wirel Commun, 9(11):3590–3600. https://doi.org/10.1109/TWC.2010.092810.091092

    Article  Google Scholar 

  83. Matolak DW, 2015. Channel characterization for unmanned aircraft systems. 9th European Conf on Antennas and Propagation, p.1–5.

  84. Matolak DW, Sun RY, 2014. Antenna and frequency diversity in the unmanned aircraft systems bands for the over-sea setting. IEEE/AIAA 33rd Digital Avionics Systems Conf, p.1–10. https://doi.org/10.1109/DASC.2014.6979495

  85. Matolak DW, Sun RY, 2017a. Air-ground channel characterization for unmanned aircraft systems. Part I: methods, measurements, and models for over-water settings. IEEE Trans Veh Technol, 66(1):26–44. https://doi.org/10.1109/TVT.2016.2530306

    Article  Google Scholar 

  86. Matolak DW, Sun RY, 2017b. Air-ground channel characterization for unmanned aircraft systems. Part III: the suburban and near-urban environments. IEEE Trans Veh Technol, 66(8):6607–6618. https://doi.org/10.1109/TVT.2017.2659651

    Article  Google Scholar 

  87. Matolak DW, Sen I, Xiong WH, et al., 2005. 5 GHz wireless channel characterization for vehicle to vehicle communications. IEEE Military Communications Conf, p.3016–3022. https://doi.org/10.1109/MILCOM.2005.1606122

  88. Meredith J, 2016. Study on Channel Model for Frequency Spectrum above 6 GHz. Technical Report TR 38900, 3GPP.

  89. Miaoudakis A, Lekkas A, Kalivas G, et al., 2005. Radio channel characterization in industrial environments and spread spectrum modem performance. IEEE Conf on Emerging Technologies and Factory Automation, p.87–93. https://doi.org/10.1109/ETFA.2005.1612506

  90. Molisch AF, 2012. Wireless Communications. John Wiley & Sons, New York.

    Google Scholar 

  91. Moral PD, 1996. Non-linear filtering: interacting particle resolution. Markov Process Rel Field, 2(4):555–581.

    MATH  Google Scholar 

  92. Nachmani E, Marciano E, Burshtein D, et al., 2017. RNN decoding of linear block codes. https://arxiv.org/abs/1702.07560

  93. Nachmani E, Marciano E, Lugosch L, et al., 2018. Deep learning methods for improved decoding of linear codes. IEEE J Sel Top Signal Process, 12(1):119–131. https://doi.org/10.1109/JSTSP.2017.2788405

    Article  Google Scholar 

  94. Navabi S, Wang CW, Bursalioglu OY, et al., 2018. Predicting wireless channel features using neural networks. IEEE Int Conf on Communications, p.1–6. https://doi.org/10.1109/ICC.2018.8422221

  95. Newhall WG, Mostafa R, Dietrich C, et al., 2003. Wideband air-to-ground radio channel measurements using an antenna array at 2 GHz for low-altitude operations. IEEE Military Communications Conf, p.1422–1427. https://doi.org/10.1109/MILCOM.2003.1290436

  96. Nikolaidis V, Moraitis N, Kanatas AG, 2016. Dual polarized MIMO LMS channel measurements and characterization in a pedestrian environment. 10th European Conf on Antennas and Propagation, p.1–5. https://doi.org/10.1109/EuCAP.2016.7481470

  97. Ono F, Takizawa K, Tsuji H, et al., 2015. S-band radio propagation characteristics in urban environment for unmanned aircraft systems. Int Symp on Antennas and Propagation, p.1–4.

  98. O’Shea TJ, Hoydis J, 2017. An introduction to machine learning communications systems. https://arxiv.org/abs/1702.00832v1

  99. Petropoulou P, Michailidis ET, Panagopoulos AD, et al., 2014. Radio propagation channel measurements for multi-antenna satellite communication systems: a survey. IEEE Antenn Propag Mag, 56(6):102–122. https://doi.org/10.1109/MAP.2014.7011023

    Article  Google Scholar 

  100. Piesiewicz R, Kleine-Ostmann T, Krumbholz N, et al., 2005. Terahertz characterisation of building materials. Electron Lett, 41(18):1002–1004. https://doi.org/10.1049/el:20052444

    Article  Google Scholar 

  101. Piesiewicz R, Jacob M, Koch M, et al., 2008. Performance analysis of future multigigabit wireless communication systems at THz frequencies with highly directive antennas in realistic indoor environments. IEEE J Sel Top Quant Electron, 14(2):421–430. https://doi.org/10.1109/JSTQE.2007.910984

    Article  Google Scholar 

  102. Pometcu L, D’Errico R, 2018. Large scale and clusters characteristics in indoor sub-THz channels. Proc 29th Annual Int Symp Personal Indoor and Mobile Radio Communications, p.1405–1409. https://doi.org/10.1109/PIMRC.2018.8580938

  103. Priebe S, Kuerner T, 2013. Stochastic modeling of THz indoor radio channels. IEEE Trans Wirel Commun, 12(9):4445–4455. https://doi.org/10.1109/TWC.2013.072313.121581

    Article  Google Scholar 

  104. Priebe S, Jastrow C, Jacob M, et al., 2011. Channel and propagation measurements at 300 GHz. IEEE Trans Antenn Propag, 59(5):1688–1698. https://doi.org/10.1109/TAP.2011.2122294

    Article  Google Scholar 

  105. Priebe S, Kannicht M, Jacob M, et al., 2013. Ultra broadband indoor channel measurements and calibrated ray tracing propagation modeling at THz frequencies. J Commun Netw, 15(6):547–558. https://doi.org/10.1109/JCN.2013.000103

    Article  Google Scholar 

  106. Priebe S, Jacob M, Kuerner T, 2014. Angular and RMS delay spread modeling in view of THz indoor communication systems. Radio Sci, 49(3):242–251. https://doi.org/10.1002/2013RS005292

    Article  Google Scholar 

  107. Quinlan JR, 1986. Induction of decision trees. Mach Learn, 1(1):81–106.

    Google Scholar 

  108. Raimundo X, Salous S, Cheema A, 2018. Indoor dual polarised radio channel characterisation in the 54 and 70 GHz bands. IET Microw Antenn Propag, 12(8):1287–1292. https://doi.org/10.1049/iet-map.2017.0711

    Article  Google Scholar 

  109. Rappaport TS, McGillem CD, 1987. Characterising the UHF factory radio channel. Electron Lett, 23(19):1015–1017. https://doi.org/10.1049/el:19870712

    Article  Google Scholar 

  110. Rappaport TS, Xing YC, MacCartney GR, et al., 2017. Overview of millimeter wave communications for fifth-generation (5G) wireless networks: with a focus on propagation models. IEEE Trans Antenn Propag, 65(12):6213–6230. https://doi.org/10.1109/TAP.2017.2734243

    Article  Google Scholar 

  111. Rasmussen CE, 2003. Gaussian processes in machine learning. In: Bousquet O, Luxburg U, Rätsch G (Eds.), Advanced Lectures on Machine Learning. Springer, Berlin Heidelberg, p.63–71. https://doi.org/10.1007/978-3-540-28650-9_4

    Google Scholar 

  112. Rey S, Eckhardt JM, Peng B, et al., 2017. Channel sounding techniques for applications in THz communications: a first correlation based channel sounder for ultrawideband dynamic channel measurements at 300 GHz. Proc 9th Int Con gress on Ultra Modern Telecommunications and Control Systems and Workshops, p.449–453. https://doi.org/10.1109/ICUMT.2017.8255203

  113. Richter F, Fehske AJ, Fettweis GP, 2009. Energy efficiency aspects of base station deployment strategies for cellular networks. IEEE 70th Vehicular Technology Conf, p.1–5. https://doi.org/10.1109/VETECF.2009.5379031

  114. Rieche M, Ihlow A, Arndt D, et al., 2015. Modeling of the land mobile satellite channel considering the terminal’s driving direction. Int J Antenn Propag, 2015:372124. https://doi.org/10.1155/2015/372124

    Article  Google Scholar 

  115. Samimi MK, Rappaport TS, MacCartney GR, 2015. Probabilistic omnidirectional path loss models for millimeter-wave outdoor communications. IEEE Wirel Commun Lett, 4(4):357–360. https://doi.org/10.1109/LWC.2015.2417559

    Article  Google Scholar 

  116. Series M, 2015. IMT Vision-Framework and Overall Objectives of the Future Development of IMT for 2020 and Beyond. Report M.2083–0, ITU-R, Geneva, Switzerland.

    Google Scholar 

  117. Sexton D, Mahony M, Lapinski M, et al., 2005. Radio channel quality in industrial wireless sensor networks. Sensors for Industry Conf, p.88–94. https://doi.org/10.1109/SICON.2005.257875

  118. Shafin R, Liu LJ, Chandrasekhar V, et al., 2019. Artificial intelligence-enabled cellular networks: a critical path to beyond-5G and 6G. https://arxiv.org/abs/1907.07862

  119. Simunek M, Pechac P, Fontan FP, 2011. Excess loss model for low elevation links in urban areas for UAVs. Radioengineering, 20(3):561–568.

    Google Scholar 

  120. Solomitckii D, Orsino A, Andreev S, et al., 2018. Characterization of mmWave channel properties at 28 and 60 GHz in factory automation deployments. IEEE Wireless Communications and Networking Conf, p.1–6. https://doi.org/10.1109/WCNC.2018.8377337

  121. Strinati EC, Barbarossa S, Gonzalez-Jimenez JL, et al., 2019. 6G: the next frontier. https://arxiv.org/abs/1901.03239

  122. Sun RY, Matolak DW, 2017. Air-ground channel characterization for unmanned aircraft systems. Part II: hilly and mountainous settings. IEEE Trans Veh Technol, 66(3):1913–1925. https://doi.org/10.1109/TVT.2016.2585504

    Article  Google Scholar 

  123. Tang P, Zhang J, Molisch AF, et al., 2018. Estimation of the K-factor for temporal fading from single-snapshot wideband measurements. IEEE Trans Veh Technol, 68(1):49–63. https://doi.org/10.1109/TVT.2018.2878352

    Article  Google Scholar 

  124. Tanghe E, Joseph W, Verloock L, et al., 2008. The industrial indoor channel: large-scale and temporal fading at 900, 2400, and 5200 MHz. IEEE Trans Wirel Commun, 7(7):2740–2751. https://doi.org/10.1109/TWC.2008.070143

    Article  Google Scholar 

  125. Tu HD, Shimamoto S, 2009. A proposal of wide-band air-to-ground communication at airports employing 5-GHz band. IEEE Wireless Communications and Networking Conf, p.1–6. https://doi.org/10.1109/WCNC.2009.4917538

  126. Vogel WJ, Goldhirsh J, 1986. Tree attenuation at 869 MHz derived from remotely piloted aircraft measurements. IEEE Trans Antenn Propag, 34(12):1460–1464. https://doi.org/10.1109/TAP.1986.1143781

    Article  Google Scholar 

  127. Vogel WJ, Goldhirsh J, 1988. Fade measurements at L-band and UHF in mountainous terrain for land mobile satellite systems. IEEE Trans Antenn Propag, 36(1):104–113. https://doi.org/10.1109/8.1081

    Article  Google Scholar 

  128. Vogel WJ, Goldhirsh J, 1993. Earth-satellite tree attenuation at 20 GHz: foliage effects. Electron Lett, 29(18):1640–1641. https://doi.org/10.1049/el:19931092

    Article  Google Scholar 

  129. Wang CX, Bian J, Sun J, et al., 2018. A survey of 5G channel measurements and models. IEEE Commun Surv Tutor, 20(4):3142–3168. https://doi.org/10.1109/COMST.2018.2862141

    Article  Google Scholar 

  130. Wang Z, Li L, Xu Y, et al., 2018. Handover control in wireless systems via asynchronous multiuser deep reinforcement learning. IEEE Int Thing J, 5(6):4296–4307. https://doi.org/10.1109/JIOT.2018.2848295

    Article  Google Scholar 

  131. Wang ZY, Shen C, 2017. Small cell transmit power assignment based on correlated bandit learning. IEEE J Sel Area Commun, 35(5):1030–1045. https://doi.org/10.1109/JSAC.2017.2679660

    Article  Google Scholar 

  132. Watkins C, 1989. Learning from Delayed Rewards. PhD Thesis, University of Cambridge, Cambridge, UK.

    Google Scholar 

  133. Wentz M, Stojanovic M, 2015. A MIMO radio channel model for low-altitude air-to-ground communication systems. IEEE 82nd Vehicular Technology Conf, p.1–6. https://doi.org/10.1109/VTCFall.2015.7390797

  134. Willink TJ, Squires CC, Colman GW, et al., 2015. Measurement and characterization of low-altitude air-to-ground MIMO channels. IEEE Trans Veh Technol, 65(4):2637–2648. https://doi.org/10.1109/TVT.2015.2419738

    Article  Google Scholar 

  135. WP5D I, 2017. Guidelines for Evaluation of Radio Interface Technologies for IMT-2020. Report M.2412, ITU-R, Geneva, Switzerland.

    Google Scholar 

  136. Xie YJ, Fang YG, 2000. A general statistical channel model for mobile satellite systems. IEEE Trans Veh Technol, 49(3):744–752. https://doi.org/10.1109/25.845094

    Article  Google Scholar 

  137. Yang GS, Zhang Y, He ZW, et al., 2019. Machine-learning-based prediction methods for path loss and delay spread in air-to-ground millimetre-wave channels. IET Microw Antenn Propag, 13(8):1113–1121. https://doi.org/10.1049/iet-map.2018.6187

    Article  Google Scholar 

  138. Yanmaz E, Kuschnig R, Bettstetter C, 2011. Channel measurements over 802.11a-based UAV-to-ground links. IEEE GLOBECOM Workshops, p.1280–1284. https://doi.org/10.1109/GLOCOMW.2011.6162389

  139. Zhang C, Hui YN, 2011. Broadband air-to-ground communications with adaptive MIMO datalinks. IEEE/AIAA 30th Digital Avionics Systems Conf, p.4D4-1. https://doi.org/10.1109/DASC.2011.6095912

  140. Zhang J, 2016. The interdisciplinary research of big data and wireless channel: a cluster-nuclei based channel model. China Commun, 13(S2):14–26. https://doi.org/10.1109/CC.2016.7833457

    Article  Google Scholar 

  141. Zhang J, Pan C, Pei F, et al., 2014. Three-dimensional fading channel models: a survey of elevation angle research. IEEE Commun Mag, 52(6):218–226. https://doi.org/10.1109/MCOM.2014.6829967

    Article  Google Scholar 

  142. Zhang JH, Tang P, Tian L, et al., 2017a. 6–100 GHz research progress and challenges for fifth generation (5G) and future wireless communication from channel perspective. Sci China Inform Sci, 60(8):080301. https://doi.org/10.1007/s11432-016-9144-x

    Article  Google Scholar 

  143. Zhang JH, Zhang YX, Yu YW, et al., 2017b. 3D MIMO: how much does it meet our expectations observed from channel measurements? IEEE J Sel Areas Commun, 35(8):1887–1903. https://doi.org/10.1109/JSAC.2017.2710758

    Article  Google Scholar 

  144. Zhang JH, Zheng Z, Zhang YX, et al., 2018. 3D MIMO for 5G NR: several observations from 32 to massive 256 antennas based on channel measurement. IEEE Commun Mag, 56(3):62–70. https://doi.org/10.1109/MCOM.2018.1700846

    Article  Google Scholar 

  145. Zhang P, Niu K, Tian H, et al., 2019. The outlook for 6G mobile communication technologies. J Commun, 4(1):145–152.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Pan Tang.

Additional information

Contributors

Jian-hua ZHANG designed the research, and revised and edited the final version. Pan TANG leaded the drafting of the manuscript. Li YU, Tao JIANG, and Lei TIAN helped draft the manuscript.

Compliance with ethics guidelines

Jian-hua ZHANG, Pan TANG, Li YU, Tao JIANG, and Lei TIAN declare that they have no conflict of interest.

Project supported by the National Key R&D Program of China (No. 2018YFB1801101), the National Science Fund for Distinguished Young Scholars, China (No. 61925102), the Key Project of State Key Lab of Networking and Switching Technology, China (No. NST20180105), Huawei, and ZTE Corporation

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, Jh., Tang, P., Yu, L. et al. Channel measurements and models for 6G: current status and future outlook. Front Inform Technol Electron Eng 21, 39–61 (2020). https://doi.org/10.1631/FITEE.1900450

Download citation

Key words

  • Channel measurements
  • Channel models
  • Sixth generation
  • Terahertz
  • Industrial Internet of Things
  • Space-air-ground integrated network
  • Machine learning

CLC number

  • TN929.5