Measurement and Statistical Analysis of Distinguishable Multipaths in Underground Tunnels

Compared with the line-of-sight (LOS) condition, the multipath effect is more serious in the non-line-of-sight (NLOS) condition. Therefore, the LOS and NLOS identification is necessary for the multipath analysis of signal propagation. The commonly used method is the support vector machine (SVM) method with high computational complexity. To tackle this problem, this paper adopts the SVM classifier based on fewer selected features of the normalized power delay profile (PDP). Therein, the PDP can be obtained using the sliding correlation method. The results show that the SVM-based classifier can achieve high accuracy on LOS and NLOS identification. We then analyze the impact of the signal-to-noise ratio (SNR) and transmitting-receiving (Tx-to-Rx) distance on distinguishable multipaths under LOS and NLOS conditions. According to statistical measurement results, a function of distinguishable multipath numbers is established. Finally, we investigate the multipath power and delay parameters of average delay spread and root mean square (RMS) delay spread based on multipath results. The outcomes of this paper provide a useful support for analyzing signal propagation characteristics.

[1]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[2]  Giuseppe Aceto,et al.  MIMETIC: Mobile encrypted traffic classification using multimodal deep learning , 2019, Comput. Networks.

[3]  Marc Hesse,et al.  Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods , 2020 .

[4]  Ta-Wen Kuan,et al.  VLSI Design of an SVM Learning Core on Sequential Minimal Optimization Algorithm , 2012, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[5]  Jiping Li,et al.  Non-parametric Non-line-of-Sight Identification and Estimation for Wireless Location , 2012, 2012 International Conference on Computer Science and Service System.

[6]  Tao Tang,et al.  Measurements and Analysis of Large-Scale Fading Characteristics in Curved Subway Tunnels at 920 MHz, 2400 MHz, and 5705 MHz , 2015, IEEE Transactions on Intelligent Transportation Systems.

[7]  Xiaojun Zhang,et al.  Measurement and simulation of wideband channel characterization in the underground tunnel environment , 2017, 2017 Sixth Asia-Pacific Conference on Antennas and Propagation (APCAP).

[8]  Chenming Zhou,et al.  RF Propagation in Mines and Tunnels: Extensive measurements for vertically, horizontally, and cross-polarized signals in mines and tunnels. , 2015, IEEE Antennas and Propagation Magazine.

[9]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[10]  Steven Liu,et al.  UWB NLOS identification with feature combination selection based on genetic algorithm , 2019, 2019 IEEE International Conference on Consumer Electronics (ICCE).

[11]  Ke Guan,et al.  Measurement of Distributed Antenna Systems at 2.4 GHz in a Realistic Subway Tunnel Environment , 2012, IEEE Transactions on Vehicular Technology.

[12]  Youyun Xu,et al.  Path Loss Modeling for Train-to-Train Communications in Subway Tunnels at 900/2400 MHz , 2019, IEEE Antennas and Wireless Propagation Letters.

[13]  Bo Ai,et al.  Machine Learning-Enabled LOS/NLOS Identification for MIMO Systems in Dynamic Environments , 2020, IEEE Transactions on Wireless Communications.

[14]  Bo Ai,et al.  Propagation channel measurements and analysis at 2.4 GHz in subway tunnels , 2013 .

[15]  Kyandoghere Kyamakya,et al.  NLOS detection algorithms for Ultra-Wideband localization , 2007, 2007 4th Workshop on Positioning, Navigation and Communication.

[16]  Moe Z. Win,et al.  NLOS identification and mitigation for localization based on UWB experimental data , 2010, IEEE Journal on Selected Areas in Communications.

[17]  Giuseppe Aceto,et al.  Mobile Encrypted Traffic Classification Using Deep Learning: Experimental Evaluation, Lessons Learned, and Challenges , 2019, IEEE Transactions on Network and Service Management.

[18]  Lorenzo Rubio,et al.  Path Loss Characterization for Vehicular Communications at 700 MHz and 5.9 GHz Under LOS and NLOS Conditions , 2014, IEEE Antennas and Wireless Propagation Letters.

[19]  José A. García-Naya,et al.  NLOS Classification Based on RSS and Ranging Statistics Obtained from Low-Cost UWB Devices , 2019, 2019 27th European Signal Processing Conference (EUSIPCO).

[20]  Andreas F. Molisch,et al.  Angular Information-Based NLOS/LOS Identification for Vehicle to Vehicle MIMO System , 2019, 2019 IEEE International Conference on Communications Workshops (ICC Workshops).

[21]  José Manuel Riera,et al.  A survey on future railway radio communications services: challenges and opportunities , 2015, IEEE Communications Magazine.

[22]  Kup-Sze Choi,et al.  A Transfer-Based Additive LS-SVM Classifier for Handling Missing Data , 2020, IEEE Transactions on Cybernetics.

[23]  Ji Gao,et al.  Fast training Support Vector Machines using parallel sequential minimal optimization , 2008, 2008 3rd International Conference on Intelligent System and Knowledge Engineering.

[24]  Gorazd Kandus,et al.  A Survey of Radio Propagation Modeling for Tunnels , 2014, IEEE Communications Surveys & Tutorials.

[25]  S. Affes,et al.  Radio Wave Characterization and Modeling in Underground Mine Tunnels , 2008, IEEE Transactions on Antennas and Propagation.

[26]  Markus Rupp,et al.  Society in motion: challenges for LTE and beyond mobile communications , 2016, IEEE Communications Magazine.

[27]  Jinxing Li,et al.  Radio channel measurements and analysis at 2.4/5GHz in subway tunnels , 2015 .

[28]  Ole Kiel Jensen,et al.  Non-Line-of-Sight Identification for UWB Indoor Positioning Systems using Support Vector Machines , 2019, 2019 IEEE MTT-S International Wireless Symposium (IWS).

[29]  A. G. Emslie,et al.  Theory of the propagation of UHF radio waves in coal mine tunnels , 1975 .

[30]  WymeerschHenk,et al.  NLOS identification and mitigation for localization based on UWB experimental data , 2010 .