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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Channel modeling based on multilayer artificial neural network in metro tunnel environments

  • Jingyuan Qian (Shanghai Institute for Advanced Communication and Data Science, Shanghai University) ;
  • Asad Saleem (College of Information Science and Electronic Engineering, Zhejiang Univerity-University oof Illinois Institute at Urbana Champaign Institute) ;
  • Guoxin Zheng (Shanghai Institute for Advanced Communication and Data Science, Shanghai University)
  • Received : 2022.03.25
  • Accepted : 2022.06.13
  • Published : 2023.08.10
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Abstract

Traditional deterministic channel modeling is accurate in prediction, but due to its complexity, improving computational efficiency remains a challenge. In an alternative approach, we investigated a multilayer artificial neural network (ANN) to predict large-scale and small-scale channel characteristics in metro tunnels. Simulated high-precision training datasets were obtained by combining measurement campaign with a ray tracing (RT) method in a metro tunnel. Performance on the training data was used to determine the number of hidden layers and neurons of the multilayer ANN. The proposed multilayer ANN performed efficiently (10 s for training; 0.19 ms for prediction), and accurately, with better approximation of the RT data than the single-layer ANN. The root mean square errors (RMSE) of path loss (2.82 dB), root mean square delay spread (0.61 ns), azimuth angle spread (3.06°), and elevation angle spread (1.22°) were impressive. These results demonstrate the superior computing efficiency and model complexity of ANNs.

Keywords

Acknowledgement

This research was supported by National Natural Science Foundation of China (61871261) and the Natural Science Foundation of Shanghai (22ZR1422200).

References

  1. J. Li, Y. Zhao, J. Zhang, R. Jiang, C. Tao, and Z. Tan, Radio channel measurements and analysis at 2.4/5GHz in subway tunnels, China Commun. 12 (2015), no. 1, 36-45. https://doi.org/10.1109/CC.2015.7084382
  2. Y. Wu, G. Zheng, A. Saleem, and Y. P. Zhang, An experimental study of MIMO performance using leaky coaxial cables in a tunnel, IEEE Ant. Propag. Lett. 16 (2017), 1663-1666. https://doi.org/10.1109/LAWP.2017.2662209
  3. B. Ai, K. Guan, Z. Zhong, C. F. Lopez, L. Zhang, C. BrisoRodriguez, and R. He, Measurement and analysis of extra propagation loss of tunnel curve, IEEE Trans. Veh. Technol. 65 (2016), no. 4, 1847-1858. https://doi.org/10.1109/TVT.2015.2425218
  4. Y. P. Zhang, Novel model for propagation loss prediction in tunnels, IEEE Trans. Veh. Technol. 52 (2003), no. 5, 1308-1314. https://doi.org/10.1109/TVT.2003.816647
  5. D. Dudley, M. Lienard, S. Mahmoud, and P. Degauque, Wireless propagation in tunnels, IEEE Ant. Propag. Mag. 49 (2007), no. 2, 11-26.
  6. S. Loyka, Multiantenna capacities of waveguide and cavity channels, IEEE Trans. Veh. Technol. 54 (2005), no. 3, 863-872. https://doi.org/10.1109/TVT.2005.844640
  7. P. V. Nikitin, D. D. Stancil, and E. A. Erosheva, Estimating the number of modes in multimode waveguide propagation environment, (IEEE International Symposium on Antennas and Propagation, Spokane, WA, USA), Aug. 2011, pp. 1662-1665.
  8. A. Hrovat, G. Kandus, and T. Javornik, A survey of radio propagation modeling for tunnels, IEEE Commun. Surv. Tutor. 16 (2014), no. 2, 658-669. https://doi.org/10.1109/SURV.2013.091213.00175
  9. Y. Wang, S. Safavi-Naeini, and S. K. Chaudhuri, A hybrid technique based on combining ray tracing and FDTD methods for site-specific modeling of indoor radio wave propagation, IEEE Trans. Ant. Propag. 48 (2000), no. 5, 743-754. https://doi.org/10.1109/8.855493
  10. A. Fourie and D. Nitch, SuperNEC: Antenna and indoorpropagation simulation program, IEEE Ant. Propag. Mag. 42 (2000), no. 3, 31-48. https://doi.org/10.1109/74.848946
  11. R. Martelly and R. Janaswamy, Modeling radio transmission loss in curved, branched and rough-walled tunnels with the ADI-PE method, IEEE Trans. Ant. Propag. 58 (2010), no. 6, 2037-2045. https://doi.org/10.1109/TAP.2010.2046862
  12. M. Jia, G. Zheng, and W. Ji, A new model for predicting the characteristic of RF propagation in rectangular tunnel, (International Conference on Wireless Communications, Networking and Mobile Computing. (Dalian, China), Oct. 2008, pp. 1-4.
  13. S. H. Chen and S. K. Jeng, SBR image approach for radio wave propagation in tunnels with and without traffic, IEEE Trans. Veh. Technol. 45 (1996), no. 3, 570-578. https://doi.org/10.1109/25.533772
  14. F. M. Pallares, F. J. P. Juan, and L. Juan-Llacer, Analysis of path loss and delay spread at 900 MHz and 2.1 GHz while entering tunnels, IEEE Trans. Veh. Technol. 50 (2001), no. 3, 767-776. https://doi.org/10.1109/25.933311
  15. M. H. Kermani, and M. Kamarei, A ray-tracing method for predicting delay spread in tunnel environments, (IEEE International Conference on Personal Wireless Communications Conference Proceedings, Hyderabad, India), Aug. 2000, pp. 538-542.
  16. E. Masson, P. Combeau, M. Berbineau, and R. Vauzelle, Measurements and simulations comparisons of radio wave propagation in arch-shaped tunnels for mass transit applications, (9th International Conference on Intelligent Transport Systems Telecommunications(Lille, France), Oct. 2009, pp. 37-41.
  17. E. Ostlin, H. Zepernick, and H. Suzuki, Macrocell path-loss prediction using artificial neural networks, IEEE Trans. Veh. Technol. 59 (2010), no. 6, 2735-2747. https://doi.org/10.1109/TVT.2010.2050502
  18. L. Azpilicueta, M. Rawat, K. Rawat, F. M. Ghannouchi, and F. Falcone, A ray launching-neural network approach for radio wave propagation analysis in complex indoor environments, IEEE Trans. Ant. Propag. 62 (2014), no. 5, 2777-2786. https://doi.org/10.1109/TAP.2014.2308518
  19. A. Neskovic, N. Neskovic, and D. Paunovic, Indoor electric field level prediction model based on the artificial neural networks, IEEE Commun. Lett. 4 (2000), no. 6, 190-192. https://doi.org/10.1109/4234.848409
  20. G. P. Ferreira, L. J. Matos, and J. M. M. Silva, Improvement of outdoor signal strength prediction in UHF band by artificial neural network, IEEE Trans. Ant. Propag. 64 (2016), no. 12, 5404-5410. https://doi.org/10.1109/TAP.2016.2617379
  21. S. I. Popoola, A. Jefia, A. A. Atayero, O. Kingsley, N. Faruk, O. F. Oseni, and R. O. Abolade, Determination of neural network parameters for path loss prediction in very high frequency wireless channel, IEEE Access 7 (2019), 150462-150483. https://doi.org/10.1109/ACCESS.2019.2947009
  22. J. Liu, X. Jin, F. Dong, L. He, and H. Liu, Fading channel modelling using single-hidden layer feedforward neural networks, Multidim. Syst. Signal Process. 28 (2017), 885-903. https://doi.org/10.1007/s11045-015-0380-1
  23. J. O. Eichie, O. D. Oyedum, M. O. Ajewole, and A. M. Aibinu, Comparative analysis of basic models and artificial neural network based model for path loss prediction, Pier m 61 (2017), 133-146. https://doi.org/10.2528/PIERM17060601
  24. A. Malone, and C. D. Sarris, "Electromagnetic vision": Machine intelligence models of radiowave propagation in tunnels, (IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, Boston, MA, USA), July 2018, pp. 557-558.
  25. X. Zhao, F. Du, S. Geng, N. Sun, Y. Zhang, Z. Fu, and G. Wang, Neural network and GBSM based time-varying and stochastic channel modeling for 5G millimeter wave communications, China Commun. 16 (2019), no. 6, 80-90. https://doi.org/10.23919/JCC.2019.06.007
  26. N. Sun, S. Geng, S. Li, X. Zhao, M. Wang, and S. Sun, Channel modeling by RBF neural networks for 5G Mm-wave communication, (IEEE/CIC International Conference on Communications in China, Beijing, China), Aug. 2018, pp. 768-772.
  27. L. Bai, C. X. Wang, J. Huang, Q. Xu, Y. Yang, G. Goussetis, J. Sun, and W. Zhang, Predicting wireless mmWave massive MIMO channel characteristics using machine learning algorithms, Wireless Commun. Mobile Comput. 2018 (2018), 9783863.
  28. J. Huang, C. X. Wang, L. Bai, J. Sun, Y. Yang, J. Li, O. Tirkkonen, and M. T. Zhou, A big data enabled channel model for 5G wireless communication systems, IEEE Trans. Big Data 6 (2020), no. 2, 211-222. https://doi.org/10.1109/TBDATA.2018.2884489
  29. P. Petrus, J. H. Reed, and T. S. Rappaport, Geometrical-based statistical macrocell channel model for mobile environments, IEEE Trans. Commun. 50 (2002), no. 3, 495-502. https://doi.org/10.1109/26.990911
  30. R. B. Ertel and J. H. Reed, Angle and time of arrival statistics for circular and elliptical scattering models, IEEE J. Sel. Areas Commun. 17 (1999), no. 11, 1829-1840. https://doi.org/10.1109/49.806814
  31. G. D. Kondylis, F. De Flaviis, G. J. Pottie, and Y. RahmatSamii, Indoor channel characterization for wireless communications using reduced finite difference time domain (R-FDTD), (IEEE VTS 50th Vehicular Technology Conference, Amsterdam, Netherlands), Sept. 1999, pp. 1402-1406.
  32. M. M. Rana and A. S. Mohan, Segmented-locally-one-dimensional-FDTD method for EM propagation inside large complex tunnel environments, IEEE Trans. Magn. 48 (2012), no. 2, 223-226. https://doi.org/10.1109/TMAG.2011.2177075
  33. F. Fuschini and G. Falciasecca, A mixed rays-Modes approach to the propagation in real road and railway tunnels, IEEE Trans. Ant. Propag. 60 (2012), no. 2, 1095-1105. https://doi.org/10.1109/TAP.2011.2173137
  34. C. Briso-Rodriguez, J. M. Cruz, and J. I. Alonso, Measurements and modeling of distributed antenna systems in railway tunnels, IEEE Trans. Veh. Technol. 56 (2007), no. 5, 2870-2879. https://doi.org/10.1109/TVT.2007.900500
  35. S. Wang, H. Zhao, G. Zheng, F. Zhang, A. Saleem, K. Zhang, and L. Cang, Ultra-high mobility analysis of MIMO wireless system in tunnel scenarios, IEEE Commun. Lett. 26 (2021), no. 3, 687-691.
  36. T. Zhou, H. Li, R. Sun, Y. Wang, L. Liu, and C. Tao, Simulation and analysis of propagation characteristics for tunnel trainground communications at 1.4 and 40 GHz, IEEE Access 7 (2019), 105123-105131. https://doi.org/10.1109/ACCESS.2019.2932125
  37. D. He, B. Ai, K. Guan, Z. Zhong, B. Hui, J. Kim, H. Chung, and I. Kim, Channel measurement, simulation, and analysis for high-speed railway communications in 5G millimeter-wave band, IEEE Trans. Intell. Transp. Syst. 19 (2018), no. 10, 3144- 3158. https://doi.org/10.1109/TITS.2017.2771559
  38. I-R. P.2040, Effects of building materials and structures on radiowave propagation above about 100 MHz, International Telecommunication Union Radiocommunication Sector ITU-R P, 2015.
  39. S. Geng, J. Kivinen, X. Zhao, and P. Vainikainen, Millimeterwave propagation channel characterization for short-range wireless communications, IEEE Trans. Veh. Technol. 58 (2009), no. 1, 3-13. https://doi.org/10.1109/TVT.2008.924990
  40. S. D. Li, Y. J. Liu, L. K. Lin, Z. Sheng, X. C. Sun, Z. P. Chen, and X. J. Zhang, Channel measurements and modeling at 6 GHz in the tunnel environments for 5G wireless systems, Int. J. Ant. Propag. 2017 (2017), 15130380.
  41. C. Wang, W. Ji, G. Zheng, and A. Saleem, Analysis of propagation characteristics for various subway tunnel scenarios at 28 GHz, Int. J. Ant. Propag. 2021 (2021), 7666624.
  42. T. Guillod, P. Papamanolis, and J. W. Kolar, Artificial neural network (ANN) based fast and accurate inductor modeling and design, IEEE Open J. Power Electron. 1 (2020), 284-299. https://doi.org/10.1109/OJPEL.2020.3012777
  43. Z. Sun and I. F. Akyildiz, Channel modeling and analysis for wireless networks in underground mines and road tunnels, IEEE Trans. Commun. 58 (2010), no. 6, 1758-1768. https://doi.org/10.1109/TCOMM.2010.06.080353
  44. S. Bashir, Effect of antenna position and polarization on UWB propagation channel in underground mines and tunnels, IEEE Trans. Ant. Propag. 62 (2014), no. 9, 4771-4779. https://doi.org/10.1109/TAP.2014.2334352
  45. M. Zhai, K. Zhai, H. Cui, and D. Li, Multifrequency channel characterization for curved tunnels, IEEE Ant. Wireless Propag. Lett. 20 (2021), no. 12, 2457-2460. https://doi.org/10.1109/LAWP.2021.3114553
  46. C. Briso-Rodriguez, P. Fratilescu, and Y. Xu, Path loss modeling for train-to-train communications in subway tunnels at 900/2400 MHz, IEEE Ant. Wireless Propag. Lett. 18 (2019), no. 6, 1164-1168. https://doi.org/10.1109/LAWP.2019.2911406
  47. K. Guan, Z. Zhong, B. Ai, R. He, B. Chen, Y. Li, and C. Briso-Rodriguez, Complete propagation model in tunnels, IEEE Ant. Wireless Propag. Lett. 12 (2013), no. 2013, 741-744. https://doi.org/10.1109/LAWP.2013.2270937
  48. X. Zhang, N. Sood, and C. D. Sarris, Fast radio-wave propagation modeling in tunnels with a hybrid vector parabolic equation/waveguide mode theory method, IEEE Trans. Ant. Propag. 66 (2018), no. 12, 6540-6551. https://doi.org/10.1109/TAP.2018.2864344
  49. Z. Hu, W. Ji, H. Zhao, X. Zhai, A. Saleem, and G. Zheng, Channel measurement for multiple frequency bands in subway tunnel scenario, Int. J. Ant. Propag. 2021 (2021), 9991758.
  50. C. Garcia-Pardo, J. M. Molina-Garcia-Pardo, M. Lienard, D. P. Gaillot, and P. Degauque, Double directional channel measurements in an arched tunnel and interpretation using ray tracing in a rectangular tunnel, Pier m 22 (2012), no. 22, 91-107. https://doi.org/10.2528/PIERM11070110
  51. M. Yang, B. Ai, R. He, Z. Ma, Z. Zhong, J. Wang, L. Pei, Y. Li, J. Li, and N. Wang, Non-stationary vehicular channel characterization in complicated scenarios, IEEE Trans. Veh. Technol. 70 (2021), no. 9, 8387-8400. https://doi.org/10.1109/TVT.2021.3096973
  52. A. Saleem, H. Cui, Y. He, and A. Boag, Channel Propagation Characteristics for Massive Multiple-Input/Multiple-Output Systems in a Tunnel Environment [Measurements Corner]. IEEE Ant. Propag. Mag. 64 (2022), no. 3, 126-142. https://doi.org/10.1109/map.2022.3162807
  53. M. E. Morocho-Cayamcela, H. Lee and W. Lim, Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions, IEEE Access. 7 (2019), 137184-137206. https://doi.org/10.1109/ACCESS.2019.2942390
  54. M. T. Hagan and M. B. Menhaj, Training feedforward networks with the Marquardt algorithm, IEEE Trans. Neural Netw. 5 (1997), no. 6, 989-993. https://doi.org/10.1109/90.650156
  55. G. Cerri, M. Cinalli, F. Michetti, and P. Russo, Feed forward neural networks for path loss prediction in urban environment, IEEE Trans. Ant. Propag. 52 (2004), no. 11, 3137-3139. https://doi.org/10.1109/TAP.2004.835252
  56. S. P. Sotiroudis, S. K. Goudos, K. A. Gotsis, K. Siakavara, and J. N. Sahalos, Application of a composite differential evolution algorithm in optimal neural network design for propagation path-loss prediction in mobile communication systems, IEEE Ant. Wireless Propag. Lett. 12 (2013), 364-367. https://doi.org/10.1109/LAWP.2013.2251994
  57. S. O. Ajose and A. I. Imoize, Propagation measurements and modelling at 1800 MHz in Lagos Nigeria, Int. J. Wireless Mobile Comput. 6 (2013), no. 2, 154-173.
  58. S. K. Jin, S. H. Jang, D. K. Shin, S. H. Yoon, and H. G. Jung, Performance analysis of WAVE communication for emergency broadcasting in metro environments, (Tenth International Conference on Ubiquitous and Future Networks (ICUFN). Prague, Czech Republic), July 2018, pp. 557-560.
  59. A. Saleem, and Y. He, Investigation of massive MIMO channel spatial characteristics for indoor subway tunnel environment, (Computing, Communications and IoT Applications, Shenzhen, China), Nov. 2021, pp. 162-167.
  60. R. Sun, Y. Lei, Z. Chen, and Z. Sun, Investigation of MIMO channel characteristics in tunnel at 1.4725 and 6 GHz, Radio Sci. 55 (2020), no. 10, 1-11.