Finite-state Markov wireless channel modeling for railway tunnel environments

In recent years, high-speed railways (HSRs) have developed rapidly with a high transportation capacity and high comfort level. A tunnel is a complex high-speed rail terrain environment. It is very important to establish an accurate channel propagation model for a railway tunnel environment to improve the safety of HSR operation. In this paper, a method for finite-state Markov chain (FSMC) channel modeling with least squares fitting based on non-uniform interval division is proposed. First, a path loss model is obtained according to measured data. The communication distance between the transmitter and receiver in the tunnel is non-uniformly divided into several large non-overlapping intervals based on the path loss model. Then, the Lloyd-Max quantization method is used to determine the threshold of the signal-to-noise ratio (SNR) and the channel state quantization value and obtain the FSMC state transition probability matrix. Simulation experiments show that the proposed wireless channel model has a low mean square error (MSE) and can accurately predict the received signal power in a railway tunnel environment.

[1]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[2]  Bo Ai,et al.  Measurement and Analysis of Extra Propagation Loss of Tunnel Curve , 2016, IEEE Transactions on Vehicular Technology.

[3]  Zhangdui Zhong,et al.  Finite state Markov modelling for high speed railway wireless communication channel , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[4]  Tao Tang,et al.  Finite-State Markov Modeling for Wireless Channels in Tunnel Communication-Based Train Control Systems , 2014, IEEE Transactions on Intelligent Transportation Systems.

[5]  Mohamed-Slim Alouini,et al.  Digital Communication Over Fading Channels, Second Edition , 2004 .

[6]  Cheng-Xiang Wang,et al.  Impact of Different Parameters on Channel Characteristics in a High-Speed Train Ray Tracing Tunnel Channel Model , 2017, 2017 IEEE 85th Vehicular Technology Conference (VTC Spring).

[7]  Bin Ning,et al.  Finite-state Markov modeling of tunnel channels in communication-based train control (CBTC) systems , 2013, 2013 IEEE International Conference on Communications (ICC).

[8]  Cheng Tao,et al.  Investigation of cross-correlation characteristics for multi-link channels in high-speed railway scenarios , 2018, China Communications.

[9]  Chao Shen,et al.  Finite-state Markov modeling of fading channels: A field measurement in high-speed railways , 2013, 2013 IEEE/CIC International Conference on Communications in China (ICCC).

[10]  Liang He,et al.  Finite-State Markov Modeling for High-Speed Railway Fading Channels , 2015, IEEE Antennas and Wireless Propagation Letters.

[11]  Cheng-Xiang Wang,et al.  Channel measurements and models for high-speed train wireless communication systems in tunnel scenarios: a survey , 2016, Science China Information Sciences.

[12]  M. Lax,et al.  What is a random process , 2006 .

[13]  Bo Yang,et al.  Modeling for train-ground communication channel based on WSN , 2015, The 27th Chinese Control and Decision Conference (2015 CCDC).