The Application of Markov Model Optimization Method in Wireless Channel Modeling

The Markov model has good application prospect in wireless channel modeling. However, different forms of Markov model have different performance to the current scenario.Thus an optimal selection method for the best Markov model of the wireless channel is necessary. At first, different kinds of Markov model matrix should be prepared. Then calculate the similarities of the same matrix in two adjacent time periods. Compare them to select the best Markov model. The experiments show that the optimal Markov model matrix selected can ensure highest fitness to the current scenario and most sensitive to the environment disturbance. It has certain performance in wireless channel modeling and wireless channel environment abnormal detection.

[1]  C. Charalambous,et al.  Stochastic models for long-term multipath fading channels and their statistical properties , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

[2]  Magnus Mossberg,et al.  Long-term fading channel estimation from sample covariances , 2009, Autom..

[3]  Saleem A. Kassam,et al.  Finite-state Markov model for Rayleigh fading channels , 1999, IEEE Trans. Commun..

[4]  Sebastian Magierowski,et al.  Joint Fading and Shadowing Model for Large Office Indoor WLAN Environments , 2014, IEEE Transactions on Antennas and Propagation.

[5]  Dennis Goeckel,et al.  A Markovian Model for Coarse-Timescale Channel Variation in Wireless Networks , 2016, IEEE Transactions on Vehicular Technology.

[6]  Magnus Mossberg,et al.  Fast estimators for large-scale fading channels from irregularly sampled data , 2006, IEEE Transactions on Signal Processing.

[7]  Urbashi Mitra,et al.  Active State Tracking With Sensing Costs: Analysis of Two-States and Methods for $n$-States , 2017, IEEE Transactions on Signal Processing.

[8]  H. M. Jansen,et al.  Markov-modulated Ornstein–Uhlenbeck processes , 2014, Advances in Applied Probability.

[9]  Ma Yan Relevance vector machine based on particle swarm optimization of compounding kernels in electricity load forecasting , 2010 .

[10]  Hu Sh,et al.  Outlier Detection Methods Based on Neural Network in Wireless Sensor Networks , 2014 .

[11]  Gustavo Medeiros de Araújo,et al.  An optimized Markov model to predict link quality in mobile wireless sensor networks , 2012, 2012 IEEE Symposium on Computers and Communications (ISCC).

[12]  Ergin Dinc,et al.  Path-Loss and Correlation Analysis for Space and Polarization Diversity in Surface Ducts , 2016, IEEE Transactions on Antennas and Propagation.

[13]  Hong Shen Wang,et al.  Finite-state Markov channel-a useful model for radio communication channels , 1995 .

[14]  Yuan Luo,et al.  Finite State Markov Wiretap Channel With Delayed Feedback , 2017, IEEE Transactions on Information Forensics and Security.