Machine Learning for Wireless Communication Channel Modeling: An Overview

Abstract

Channel modeling is fundamental to design wireless communication systems. A common practice is to conduct tremendous amount of channel measurement data and then to derive appropriate channel models using statistical methods. For highly mobile communications, channel estimation on top of the channel modeling enables high bandwidth physical layer transmission in state-of-the-art mobile communications. For the coming 5G and diverse Internet of Things, many challenging application scenarios emerge and more efficient methodology for channel modeling and channel estimation is very much needed. In the mean time, machine learning has been successfully demonstrated efficient handling big data. In this paper, applying machine learning to assist channel modeling and channel estimation has been introduced with evidence of literature survey.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. 1.

    Jiang, Z., He, Z., Chen, S., Molisch, A.F., Zhou, S., & Niu, Z. (2018). Inferring remote channel state information: Cramér-Rae lower bound and deep learning implementation. In IEEE Global Communications Conference (GLOBECOM), (pp. 1–7). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8648140&tag=1.

  2. 2.

    3GPP. (2017). TR 38.900 version 14.2.0 release, study on channel model for frequency spectrum above 6 GHz.

  3. 3.

    3GPP. (2018). TS 29.520, 5G system; Network data analytics services.

  4. 4.

    Kristem, V., Bas, C. U., Wang, R., & Molisch, A. F. (2018). Outdoor wideband channel measurements and modeling in the 318 GHz band. IEEE Transactions on Wireless Communications, 17(7), 4620–4633. https://doi.org/10.1109/TWC.2018.2828001.

    Article  Google Scholar 

  5. 5.

    Mao, Q., Hu, F., & Hao, Q. (2018). Deep learning for intelligent wireless networks: A comprehensive survey. IEEE Communications Surveys and Tutorials, 20(4), 2595–2621. https://doi.org/10.1109/COMST.2018.2846401.

    Article  Google Scholar 

  6. 6.

    Hu, F. (2005). Vehicle-to-vehicle and vehicle-to-infrastructure communications: A technical approach. San Francisco, CA: Taylor and Francis Group.

    Google Scholar 

  7. 7.

    Das, S. S., & Prasad, R. (2018). Evolution of air interface towards 5G: Radio access technology and performance analysis (river publishers series in communications). https://www.riverpublishers.com/book_details.php?book_id=521.

  8. 8.

    Witrisal, K., Kim, Y.-H., & Prasad, R. (2001). A new method to measure parameters of frequency-selective radio channels using power measurements. IEEE Transactions on Communicaitons, 49(10), 1788–1800.

    MATH  Article  Google Scholar 

  9. 9.

    Hara, S., & Prasad, R. (2003). Multicarrier techniques for 4G mobile communications. Norwood: Artech House.

    Google Scholar 

  10. 10.

    Zhong, Z.-D., Ai, B., Zhu, G., Wu, H., Xiong, L., Wang, F.-G., et al. (2018). Advances in high-speed rail technology. Berlin: Springer.

    Google Scholar 

  11. 11.

    Farsad, N., & Goldsmith, A. (2017). Detection algorithms for communication systems using deep learning. arXiv preprint arXiv:1705.08044.

  12. 12.

    Rappaport, T. S., Xing, Y., MacCartney, G. R., Molisch, A. F., Mellios, E., & Zhang, J. (2017). Overview of millimeter wave communications for fifth-generation (5G) wireless networkswith a focus on propagation models. IEEE Transactions on Antennas and Propagation, 65(12), 6213–6230. https://doi.org/10.1109/TAP.2017.2734243.

    Article  Google Scholar 

  13. 13.

    Tse, D., & Viswanath, P. (2005). Fundamentals of wireless communication. Cambridge: Cambridge University Press.

    Google Scholar 

  14. 14.

    Rappaport, T. T. S., DiPierro, S., & Akturan, R. (2011). Analysis and simulation of interference to vehicle-equipped digital receivers from cellular mobile terminals operating in adjacent frequencies. IEEE Transactions on Vehicular Technology, 60, 1664–1676.

    Article  Google Scholar 

  15. 15.

    Sultan, K., Ali, H., & Zhang, Z. (2018). Big data perspective and challenges in next generation networks. Future Internet, 10, 56. https://doi.org/10.3390/fi10070056.

    Article  Google Scholar 

  16. 16.

    Feukeu, E. A., Ngwira, S. M., & Zuva, T. (2015). Doppler shift signature for bpsk in a vehicular network: IEEE 802.11p. In IEEE international conference on mechatronics and automation (ICMA).

  17. 17.

    Feukeu, E. A., Ngwira, S. M., & Zuva, T. (2015). Doppler shift signature for BPSK in a vehicular network: IEEE 802.11p. In 2015 IEEE international conference on mechatronics and automation (ICMA), Beijing (pp. 1744–1749). https://doi.org/10.1109/ICMA.2015.7237749.

  18. 18.

    Zhang, Y., Wen, J., Yang, G., He, Z., & Luo, X. (2018). Air-to-air path loss prediction based on machine learning methods in urban environments. Wireless Communications and Mobile Computing, 2018, Article ID 8489326. https://doi.org/10.1155/2018/8489326.

  19. 19.

    OShea, T. J., Karra, K., & Clancy, T. C. (2017). Learning approximate neural estimators for wireless channel state information. CoRR abs/1707.06260.

  20. 20.

    Ben-Hur, A., Horn, D., Siegelmann, H. T., & Vapnik, V. (2001). Support vector clustering. Journal of Machine Learning Research, 2, 125137.

    MATH  Google Scholar 

  21. 21.

    He, H., Wen, C.-K., Jin, S., & Li, G. Y. (2018). Deep learning-based channel estimation for beamspace mmWave massive MIMO system. arxiv, p. 25.

  22. 22.

    OShea, T., & Hoydis, J. (2017). An introduction to deep learning for the physical layer. IEEE Transactions on Cognitive Communications and Networking, 3(4), 563–575. https://doi.org/10.1109/TCCN.2017.2758370.

    Article  Google Scholar 

  23. 23.

    O’Shea, T. J., Erpek, T., & Clancy, T. C. (2017). Deep learning based MIMO communications. arXiv preprint arXiv:1707.07980.

  24. 24.

    Raj, V., & Kalyani, S. (2018). Backpropagating through the air: Deep learning at physical layer without channel models. IEEE Communications Letters, 22(11), 2278–2281. https://doi.org/10.1109/LCOMM.2018.2868103.

    Article  Google Scholar 

  25. 25.

    Riihijarvi, J., & Mahonen, P. (2018). Machine learning for performance prediction in mobile cellular networks. IEEE Computational Intelligence Magazine, 13(1), 51–60. https://doi.org/10.1109/MCI.2017.2773824.

    Article  Google Scholar 

  26. 26.

    Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2012). Foundations of machine learning. Cambridge: The MIT Press.

    Google Scholar 

  27. 27.

    Lee, J. H., Kim, J., Kim, B., Yoon, D., & Choi, J. W. (2017). Robust automatic modulation classification technique for fading channels via deep neural network. Entropy, 19, 454.

    Article  Google Scholar 

  28. 28.

    Lu, T., Sun, J., Wu, K., & Yang, Z. (2018). High-speed channel modeling with machine learning methods for signal integrity analysis. IEEE Transactions on Electromagnetic Compatibility, 60(6), 1957–1964. https://doi.org/10.1109/TEMC.2017.2784833.

    Article  Google Scholar 

  29. 29.

    Shiva, N., Chenwei, W., Bursalioglu, O. Y., & Haralabos, P. (2018, February 1). Predicting wireless channel features using neural networks. In 2018 IEEE international conference on communications (ICC). From arXiv database.

  30. 30.

    Sultan, K. & Ali, H. (2017, March). Where big data meets 5G? In Proceedings of the second international conference on Internet of Things, data and cloud computing, Cambridge, UK (Vol. 2223, p. 103:1103:4).

  31. 31.

    Neumann, D., Wiese, T., & Utschick, W. (2018). Learning the MMSE channel estimator. IEEE Transactions on Signal Processing, 66(11), 2905–2917. https://doi.org/10.1109/TSP.2018.2799164.

    MathSciNet  MATH  Article  Google Scholar 

  32. 32.

    Yang, Z., Zhang, Y., Yu, J., Cai, J., & Luo, J. (2018). End-to-end multi-modal multi-task vehicle control for self-driving cars with visual perceptions. arxiv , p. 02.

  33. 33.

    Ye, H., Li, G. Y., & Juang, B. (2018). Power of deep learning for channel estimation and signal detection in OFDM systems. IEEE Wireless Communications Letters, 7(1), 114–117. https://doi.org/10.1109/LWC.2017.2757490.

    Article  Google Scholar 

  34. 34.

    Farsad, N., & Goldsmith, A. (2018). Neural network detection of data sequences in communication systems. arXiv preprint arXiv:1802.02046.

  35. 35.

    Mitchell, T. M. (1997). Machine learning. Boston, MA: McGraw-Hill.

    Google Scholar 

  36. 36.

    Ye, H., Li, G., Juang, B. H. F., & Sivanesan, K. (2018). Channel agnostic end-to-end learning based communication systems with conditional GAN. arxiv, p. 2.

  37. 37.

    Ibnkahla, M. (2000). Applications of neural networks to digital communications a survey. Signal Processing, 80(7), 1185–1215. https://doi.org/10.1016/S0165-1684(00)00030-X.

    MATH  Article  Google Scholar 

  38. 38.

    Cavalcanti, B. J., Cavalcante, G. A., Mendona, L. M., Cantanhede, G., Oliveira, M. M. M., & DAssuno, A. G. (2017). A hybrid path loss prediction model based on artificial neural networks using empirical models for LTE and LTE-A at 800 MHz and 2600 MHz. Journal of Microwaves, Optoelectronics and Electromagnetic Applications, 16(3), 708–722. https://doi.org/10.1590/2179-10742017v16i3925.

    Article  Google Scholar 

  39. 39.

    Ebhota, V. C., Isabona, J., & Srivastava, V. M. (2018). Improved adaptive signal power loss prediction using combined vector statistics based smoothing and neural network approach. Progress in Electromagnetics Research C, 82, 155–169. https://doi.org/10.2528/PIERC18011203.

    Article  Google Scholar 

  40. 40.

    Hackeling, G. (2014). Mastering machine learning with scikit-learn. Birmingham: Packt Publishing Ltd., p. 14.

  41. 41.

    Huang, H., Yang, J., Huang, H., Song, Y., & Gui, G. (2018). Deep learning for super-resolution channel estimation and DOA estimation based massive MIMO system. IEEE Transactions on Vehicular Technology, 67(9), 8549–8560. https://doi.org/10.1109/TVT.2018.2851783.

    Article  Google Scholar 

  42. 42.

    Zhao, X., Hou, C., & Wang, Q. (2013). A new SVM-based modeling method of cabin path loss prediction. International Journal of Antennas and Propagation, 2013, Article ID 279070. https://doi.org/10.1155/2013/279070.

  43. 43.

    Czink, N., Cera, P., Salo, J., Bonek, E., Nuutinen, J. & Ylitalo, J. (2006). A framework for automatic clustering of parametric MIMO channel data including path powers. In Vehicular technology conference, 2006. Vtc-2006 Fall (pp. 1–5). IEEE.

  44. 44.

    He, R., Ai, B., Molisch, A. F., Stuber, G. L., Li, Q., Zhong, Z., et al. (2018). Clustering enabled wireless channel modeling using big data algorithms. IEEE Communications Magazine, 56(5), 177–183. https://doi.org/10.1109/MCOM.2018.1700701.

    Article  Google Scholar 

  45. 45.

    Li, Y., Zhang, J., & Ma, Z. (2018). Clustering in wireless propagation channel with a statistics-based framework. In 2018 IEEE wireless communications and networking conference (WCNC). Barcelona (pp. 1–6). https://doi.org/10.1109/WCNC.2018.8377218.

  46. 46.

    Czink, N., Cera, P., Salo, J., Bonek, E., Nuutinen, U., & Ylitalo, J. (2005). Automatic clustering of MIMO channel parameters using the multi-path component distance measure, In WPMC’05, Aalborg, Denmark.

  47. 47.

    Ko, J., Cho, Y.-J., Hur, S., Kim, T., Park, J., Molisch, A. F., et al. (2017). Millimeter-wave channel measurements and analysis for statistical spatial channel model in in-building and urban environments at 28 GHz. IEEE Transactions on Wireless Communications, 16(9), 5853–5868. https://doi.org/10.1109/TWC.2017.2716924.

    Article  Google Scholar 

  48. 48.

    Guraliuc, A. R., Barsocchi, P., Potort, F., & Nepa, P. (2011). Limb movements classification using wearable wireless transceivers. IEEE Transactions on Information Technology in Biomedicine, 15(3), 474–480. https://doi.org/10.1109/TITB.2011.2118763.

    Article  Google Scholar 

  49. 49.

    Czink, N., Cera, P., Salo, J., Bonek, E., Nuutinen, J., & Ylitalo, J. (2006). Improving clustering performance using multipath component distance. Electronics Letters, 42(1), 33–45. https://doi.org/10.1049/el:20063917.

    Article  Google Scholar 

  50. 50.

    Kim, D.-J., Park, Y.-W., & Park, D.-J. (2001). A novel validity index for determination of the optimal number of clusters. IEICE Transactions on Information and Systems, 38(2), 281285.

    Google Scholar 

  51. 51.

    Molisch, A. F., Asplund, H., Heddergott, R., Steinbauer, M., & Zwick, T. (2006). The COST259 directional channel model—Part I: Overview and methodology. IEEE Transactions on Wireless Communications, 5(12), 3421–3433.

    Article  Google Scholar 

  52. 52.

    Ma, X., Zhang, J., Zhang, Y., & Ma, Z. (2017). Data scheme-based wireless channel modeling method: Motivation, principle and performance. Journal of Communications and Information Networks, 2(3), 4151.

    Article  Google Scholar 

  53. 53.

    Zheng, Q., He, R., & Huang, C. (2018). A tracking-based multipath components clustering algorithm. In 2nd URSI AT-RASC, Gran Canaria, 28 May 1 June. https://doi.org/10.1109/LAWP.2017.2740622.

  54. 54.

    Huang, C., He, R., Zhong, Z., Geng, Y. L., Li, Q., & Zhong, Z. (2017). A novel tracking based multipath component clustering algorithm. IEEE Transactions on Antennas and Propagation, 16(1), 2679–2683.

    Google Scholar 

  55. 55.

    Li, J., Ai, B., He, R., Yang, M., Wang, Q., Zhang, B., et al. (2018). Cluster-based 3-D channel modeling for massive MIMO in subway station environment. IEEE Access, 6, 6257–6272. https://doi.org/10.1109/ACCESS.2017.2779119.

    Article  Google Scholar 

  56. 56.

    Maulik, U., & Bandyopadhyay, S. (2002). Performance evaluation of some clustering algorithms and validity indices. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(12), 16501654.

    Article  Google Scholar 

  57. 57.

    Dayan, P., & Niv, Y. (2008). Reinforcement learning: The good, the bad and the ugly. Current Opinion in Neurobiology, 18, 185–96. https://doi.org/10.1016/j.conb.2008.08.003.

    Article  Google Scholar 

  58. 58.

    Ghavamzadeh, M., Mannor, S., Pineau, J., & Tamar, A. (2015). Bayesian reinforcement learning: A survey. Foundations and Trends in Machine Learning, 8(5–6), 359–483. https://doi.org/10.1561/2200000049.

    MATH  Article  Google Scholar 

  59. 59.

    Bennis, M., Debbah, M., & Poor, H. V. (2018). Ultra-reliable and low-latency wireless communication: Tail, risk and scale. arxiv, p. 126.

  60. 60.

    Bi, S., Zhang, R., Ding, Z., & Cui, S. (2015). Wireless communications in the era of big data. arxiv, p. 26.

  61. 61.

    Kumar, S., & Miikkulainen, R. (1997). Dual reinforcement q-routing: An on-line adaptive routing algorithm. In ICANNE.

  62. 62.

    Ye, H., Liang, L., Li, G. Y., Kim, J., Lu, L., & Wu, M. (2018). Machine learning for vehicular networks: Recent advances and application examples. IEEE Vehicular Technology Magazine, 13(2), 94–101. https://doi.org/10.1109/MVT.2018.2811185.

    Article  Google Scholar 

  63. 63.

    Bi, S., Zhang, R., Ding, Z., & Cui, S. (2015). Wireless communications in the era of big data. IEEE Communications Magazine, 53(10), 190–199. https://doi.org/10.1109/MCOM.2015.7295483.

    Article  Google Scholar 

  64. 64.

    Zhang, J. (2016). The interdisciplinary research of big data and wireless channel: A cluster-nuclei based channel model. China Communications, 13(Supplement 2), 14–26. https://doi.org/10.1109/CC.2016.7833457.

    Article  Google Scholar 

  65. 65.

    Ericsson. (2011). More than 50 billion connected devices; White paper. Ericsson: Stockholm, Sweden.

  66. 66.

    Wang, C., Bian, J., Sun, J., Zhang, W., & Zhang, M. (2018). A survey of 5G channel measurements and models. IEEE Communications Surveys Tutorials, 20(4), 3142–3168. https://doi.org/10.1109/COMST.2018.2862141.

    Article  Google Scholar 

  67. 67.

    Mahmood, A., Shi, K., Khatoon, S., & Xiao, M. (2013). Data mining techniques for wireless sensor networks: A survey. International Journal of Distributed Sensor Networks, 9(7), 406316. https://doi.org/10.1155/2013/406316.

    Article  Google Scholar 

  68. 68.

    Keim, D. A., Mansmann, F., Schneidewind, J., & Ziegler, H. (2006). Challenges in visual data analysis. In Tenth international conference on information visualisation (IV’06), London, England (pp. 9–16). https://doi.org/10.1109/IV.2006.31.

  69. 69.

    Karagiannis, D., & Argyriou, A. (2018). Jamming attack detection in a pair of RF communicating vehicles using unsupervised machine learning. Vehicular Communications, 13, 56–63. https://doi.org/10.1016/j.vehcom.2018.05.001.

    Article  Google Scholar 

  70. 70.

    Jiang, C., Zhang, H., Ren, Y., Han, Z., Chen, K., & Hanzo, L. (2017). Machine learning paradigms for next-generation wireless networks. IEEE Wireless Communications, 24(2), 98–105. https://doi.org/10.1109/MWC.2016.1500356WC.

    Article  Google Scholar 

  71. 71.

    Verdone, R., & Zanella, A. (2012). Pervasive mobile and ambient wireless communications. COST Action 2100. Springer.

  72. 72.

    Hogstad, B. H., Patzold, M., & Youssef, N. (2005). A MIMO mobile-to-mobile channel model: Part I—The reference model. In 2005 IEEE 16th international symposium on personal, indoor and mobile radio communications (pp. 573–578). Berlin. https://doi.org/10.1109/PIMRC.2005.1651501.

  73. 73.

    Wang, L., Liu, W., & Cheng, Y. (2009). Statistical analysis of a mobile-to-mobile rician fading channel model. IEEE Transactions on Vehicular Technology, 58(1), 32–38. https://doi.org/10.1109/TVT.2008.924999.

    Article  Google Scholar 

  74. 74.

    Cheng, L., Stancil, D. D., & Bai, F. (2013). A roadside scattering model for the vehicle-to-vehicle communication channel. IEEE Journal on Selected Areas in Communications, 31(9), 449–459. https://doi.org/10.1109/JSAC.2013.SUP.0513040.

    Article  Google Scholar 

  75. 75.

    Zajic, A. G., & Stuber, G. L. (2008). Three-dimensional modeling, simulation, and capacity analysis of spacetime correlated mobile-to-mobile channels. IEEE Transactions on Vehicular Technology, 57(4), 2042–2054. https://doi.org/10.1109/TVT.2007.912150.

    Article  Google Scholar 

  76. 76.

    Cheng, X., Wang, C., Laurenson, D. I., Salous, S., & Vasilakos, A. V. (2009). An adaptive geometry-based stochastic model for non-isotropic MIMO mobile-to-mobile channels. IEEE Transactions on Wireless Communications, 8(9), 4824–4835. https://doi.org/10.1109/TWC.2009.081560.

    Article  Google Scholar 

  77. 77.

    Matolak, D. W., & Wu, Q. (2009). Vehicle-to-vehicle channels: Are we done yet? In 2009 IEEE globecom workshops. Honolulu, HI (pp. 1–6). https://doi.org/10.1109/GLOCOMW.2009.5360694.

  78. 78.

    3GPP. (2018). Study on channel model for frequencies from 0.5 to 100 GHz (Release 15), 3rd Generation Partnership Project (3GPP), TR 38.901 V15.0.0.

  79. 79.

    Sen, I., & Matolak, D. W. (2008). Vehicle-vehicle channel models for the 5-GHz band. IEEE Transactions on Intelligent Transportation System, 9(2), 235245.

    Article  Google Scholar 

  80. 80.

    Acosta-Marum, G., & Ingram, M. A. (2007). Six time- and frequency-selective empirical channel models for vehicular wireless LANs. IEEE Vehicular Technology Magazine, 2(4), 4–11. https://doi.org/10.1109/MVT.2008.917435.

    Article  Google Scholar 

  81. 81.

    He, R., Renaudin, O., Kolmonen, V.-M., Haneda, K., Zhong, Z., Ai, B., et al. (2015). A dynamic wideband directional channel model for vehicle-to-vehicle communications. IEEE Transactions on Industrial Electronics, 62(12), 78707882.

    Article  Google Scholar 

  82. 82.

    Sun, S., MacCartney, G. R., & Rappaport, T. S. (2016). Millimeter-wave distance-dependent large-scale propagation measurements and path loss models for outdoor and indoor 5G systems. In 2016 10th European conference on antennas and propagation (EuCAP), Davos (pp. 1–5). https://doi.org/10.1109/EuCAP.2016.7481506.

  83. 83.

    Sun, S., Rappaport, T. S., Rangan, S., Thomas, T. A., Ghosh, A., Kovacs, I. Z., et al. (2016). Propagation path loss models for 5G urban micro- and macro-cellular scenarios. In 2016 IEEE 83rd vehicular technology conference (VTC2016-Spring), May.

  84. 84.

    5GCM. (2016). 5G channel model for bands up to 100 GHz. Technical report. http://www.5gworkshops.com/5G_Channel_Model_for_bands_up_to100_GHz(2015-12-6).pdf.

  85. 85.

    MacCartney, G. R., & Rappaport, T. S. (2017). Rural macrocell path loss models for millimeter wave wireless communications. IEEE Journal on Selected Areas in Communications, 35(7), 16631677.

    Article  Google Scholar 

  86. 86.

    MacCartney, G. R. Jr., Sun, S., Rappaport, T. S., Xing, Y., Yan, H., Koka, J., et al. (2016). Millimeter wave wireless communications: New results for rural connectivity. In All things cellular16: Workshop on all things cellular proceedings, in conjunction with ACM MobiCom (p. 3136).

  87. 87.

    Rangan, S., Rappaport, T. S., & Erkip, E. (2014). Millimeter-wavecellularwireless networks: Potentials and challenges. Proceedings of the IEEE, 102(3), 366385.

    Article  Google Scholar 

  88. 88.

    Sun, S. S., Rappaport, T. S., Thomas, T. A., Ghosh, A., Nguyen, H. C., Kovacs, I. Z., et al. (2016). Investigation of prediction accuracy, sensitivity, and parameter stability of large-scale propagation path loss models for 5G wireless communications. IEEE Transactions on Vehicular Technology, 65(5), 1–18.

    Article  Google Scholar 

  89. 89.

    Wang, Y., Narasimha, M., & Heath, R. W. Jr. (2018). MmWave beam prediction with situational awareness: A machine learning approach. arxiv, p. 126.

  90. 90.

    Burghal, D., Wang, R., & Molisch, A. F. (2018). Band assignment in dual band systems: A learning-based approach, arXiv:1810.01534 [eess.SP].

  91. 91.

    Ostlin, E., Zepernick, H.-J., & Suzuki, H. (2010). Macrocell path-loss prediction using artificial neural networks. IEEE Transactions on Vehicular Technology, 59(6), 2735–2747.

    Article  Google Scholar 

  92. 92.

    Berardi, V. L., & Zhang, G. P. (2003). An empirical investigation of bias and variance in time series forecasting: Modeling considerations and error evaluation. IEEE Transactions on Neural Networks, 14(3), 668–679. https://doi.org/10.1109/TNN.2003.810601.

    Article  Google Scholar 

  93. 93.

    Lan, Z., Sum, C.-S., Wang, J., Baykas, T., Kojima, F., Nakase, H., et al. (2009). Relay with deflection routing for effective throughput improvement in gbps millimeter-wave wpan systems. IEEE Journal on Selected Areas in Communications, 27, 1453–1465.

    Article  Google Scholar 

  94. 94.

    3GPP TSG RAN Plenary. Study on 3D-channel model for elevation beamforming and FD-MIMO studies for LTE. http://www.3gpp.org/DynaReport/FeatureOrStudyItemFile-580042.htm.

  95. 95.

    Burghal, D., & Molisch, A. F. (2018). Rate and outage probability in dual band systems with prediction-based band switching. IEEE Wireless Communications Letters, 7(5), 872–875.

    Article  Google Scholar 

Download references

Acknowledgements

Saud Aldossari expresses a great appreciation to Prince Sattam bin Abdulaziz University for their support of providing scholarship. Kwang-Cheng Chen appreciates FC2 Collaborative Seed Grant for the support of research.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Saud Mobark Aldossari.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Aldossari, S.M., Chen, KC. Machine Learning for Wireless Communication Channel Modeling: An Overview. Wireless Pers Commun 106, 41–70 (2019). https://doi.org/10.1007/s11277-019-06275-4

Download citation

Keywords

  • Machine learning
  • Channel modeling
  • 5G
  • MmWave
  • Mobile communications
  • Indoor/outdoor communication systems
  • Regression
  • Deep neural network