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Non-stationary properties based on Markov chains are provided to characterize the wireless propagation mechanism of high-speed railway(HSR) under viaduct scenarios.This Markov modeling method reveals the temporal statistical behaviors of the persistence process corresponding to resolvable multipath components.Using the channel measurement data on Beijing–Tianjin HSR at 2.35 GHz,the steady-state probability matrices and the transition probability matrices of Markov chains are specified.The non-stationary channel parameters are informative for the link-level simulation and can be used for prototype verification for non-stationary HSR channels.In addition,we propose and investigate the concepts of the medium-scale fading entropy and lifetime,which quantitatively describe the degrees of uncertainty and stability,respectively.Furthermore,our provided models are compared with the experimental results by means of the Kullback–Leibler(KL) distance.It shows that the simulated results match the measured data very well.
Non-stationary properties based on Markov chains are provided to characterize the wireless propagation mechanism of high-speed railway (HSR) under viaduct scenarios. This Markov modeling method reveals the temporal statistical behavior of the persistence process corresponding to resolvable multipath components. Using the channel measurement data on Beijing-Tianjin HSR at 2.35 GHz, the steady-state probability matrices and the transition probability matrices of Markov chains are specified. The non-stationary channel parameters are informative for the link-level simulation and can be used for prototype verification for non-stationary HSR channels.In addition, we propose and investigate the concepts of the medium-scale fading entropy and lifetime, which quantitatively describe the degrees of uncertainty and stability, respectively. Future models, our provided models are compared with the experimental results by means of the Kullback-Leibler (KL) distance. It shows that the simulated results match the measured data very well.