Fast Simulation of Vehicular Channels Using Finite-State Markov Models

Designing an accurate vehicular channel simulator which generates samples at the high speed is a key issue to the vehicular communications transmission scheme and protocol design. Although the finite-state Markov channel (FSMC) models have been extensively investigated to describe fading channels, FSMC models cannot show the combined effect of shadowing and small-scale fading on the temporal statistical characteristic of the vehicular channel. In this letter, a vehicular channel simulator based on the FSMC is designed, which incorporates the impacts of shadowing with small-scale fading and movement speed on the temporal statistical characteristic. The steady-state probabilities and the closed-form expressions of state transition probabilities between channel states are derived based on the approximation of Lognormal–Nakagami distribution. To validate the accuracy of the proposed FSMC channel simulator, the derived model parameters and second-order statistical parameters of the simulated channel are compared with the real field measurements of high-speed vehicular channels.

[1]  Lorenzo Rubio,et al.  Estimation of the Composite Fast Fading and Shadowing Distribution Using the Log-Moments in Wireless Communications , 2013, IEEE Transactions on Wireless Communications.

[2]  Gunes Karabulut Kurt,et al.  Finite-State Markov Channel Based Modeling of RF Energy Harvesting Systems , 2017, IEEE Transactions on Vehicular Technology.

[3]  Zhangdui Zhong,et al.  Cluster-Based Nonstationary Channel Modeling for Vehicle-to-Vehicle Communications , 2017, IEEE Antennas and Wireless Propagation Letters.

[4]  Fredrik Tufvesson,et al.  Vehicular Channel Characterization and Its Implications for Wireless System Design and Performance , 2011 .

[5]  Cecilio Pimentel,et al.  Modeling Fading Channels With Binary Erasure Finite-State Markov Channels , 2017, IEEE Transactions on Vehicular Technology.

[6]  Xiang Cheng,et al.  A 3-D Geometry-Based Stochastic Model for UAV-MIMO Wideband Nonstationary Channels , 2019, IEEE Internet of Things Journal.

[7]  Guo Xie,et al.  An Optimal Communications Protocol for Maximizing Lifetime of Railway Infrastructure Wireless Monitoring Network , 2018, IEEE Transactions on Industrial Informatics.

[8]  Xiang Cheng,et al.  Wideband Channel Modeling and Intercarrier Interference Cancellation for Vehicle-to-Vehicle Communication Systems , 2013, IEEE Journal on Selected Areas in Communications.

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

[10]  Yuanguo Bi,et al.  Toward 5G Spectrum Sharing for Immersive-Experience-Driven Vehicular Communications , 2017, IEEE Wireless Communications.

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

[12]  Gordon L. Stuber,et al.  Principles of mobile communication (2nd ed.) , 2001 .

[13]  Matthias Pätzold,et al.  Geometry-Based Statistical Modeling of Non-WSSUS Mobile-to-Mobile Rayleigh Fading Channels , 2018, IEEE Transactions on Vehicular Technology.

[14]  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.

[15]  Fredrik Tufvesson,et al.  Time- and Frequency-Varying $K$-Factor of Non-Stationary Vehicular Channels for Safety-Relevant Scenarios , 2013, IEEE Transactions on Intelligent Transportation Systems.

[16]  P. Takis Mathiopoulos,et al.  Fast simulation of diversity Nakagami fading channels using finite-state Markov models , 2003, IEEE Trans. Broadcast..