Fast Time-Varying Channel Model Research for Data Processing of Wireless
ABSTRACT
With the complexity and uncertainty of mobile communication network environment, solving the classical mathematical analysis also becomes more complicated. The model tree of basis function method based on Fourier series is proposed in this paper. Model tree method is the improvement of regression tree analysis. Basis function applied here is four-order Fourier series. When the Fourier coefficients are calculated, the Gauss elimination method is implemented for solving equations. The complexity of the algorithm is n3log(n).
With the complexity and uncertainty of mobile communication network environment, solving the classical mathematical analysis also becomes more complicated. The model tree of basis function method based on Fourier series is proposed in this paper. Model tree method is the improvement of regression tree analysis. Basis function applied here is four-order Fourier series. When the Fourier coefficients are calculated, the Gauss elimination method is implemented for solving equations. The complexity of the algorithm is n3log(n).
Cite this paper
Liu, H. and Chen, H. (2015) Fast Time-Varying Channel Model Research for Data Processing of Wireless. Advances in Pure Mathematics, 5, 442-449. doi: 10.4236/apm.2015.58043.
Liu, H. and Chen, H. (2015) Fast Time-Varying Channel Model Research for Data Processing of Wireless. Advances in Pure Mathematics, 5, 442-449. doi: 10.4236/apm.2015.58043.
References
[1] Wu, W.L. (2009) The Principle of Mobile Communication. 2nd Edition, Electronic Industry Press, 1. [2] Fan, C.X. (2013) The Principle of Communication. 6th Edition, National Defense Industry Press, 8. [3] Hrycak, T., et al. (2010) Low Complexity Equalization for Doubly Selective Channels Modeled by a Basis Expansion. IEEE Transactions on Signal Processing, 58, 5706-5719.
http://dx.doi.org/10.1109/TSP.2010.2063426 [4] Das, S. (2009) Mathematical Methods for Wireless Channel Estimation and Equalization. Dissertation, University of Vienna, Vienna. [5] Ding, L.J. and Cheng, Q.Y. (2005) Numerical Calculation Method. Beijing Institute of Technology Press, Beijing, 3. [6] Zheng, Y.R. and Xiao, C.S. (2002) Improved Models for the Generation of Multiple Uncorrelated Rayleigh Fading Waveforms. IEEE Communications Letters, 6, 256-258. [7] Zhao, P.M. (2012) Thinking Python Like Computer Scientists. People’s Posts and Telecommunications Press, Beijing. [8] Chen, J. (2011) MATLAB Book. Electronic Industry Press, Beijing.
[1] Wu, W.L. (2009) The Principle of Mobile Communication. 2nd Edition, Electronic Industry Press, 1. [2] Fan, C.X. (2013) The Principle of Communication. 6th Edition, National Defense Industry Press, 8. [3] Hrycak, T., et al. (2010) Low Complexity Equalization for Doubly Selective Channels Modeled by a Basis Expansion. IEEE Transactions on Signal Processing, 58, 5706-5719.
http://dx.doi.org/10.1109/TSP.2010.2063426 [4] Das, S. (2009) Mathematical Methods for Wireless Channel Estimation and Equalization. Dissertation, University of Vienna, Vienna. [5] Ding, L.J. and Cheng, Q.Y. (2005) Numerical Calculation Method. Beijing Institute of Technology Press, Beijing, 3. [6] Zheng, Y.R. and Xiao, C.S. (2002) Improved Models for the Generation of Multiple Uncorrelated Rayleigh Fading Waveforms. IEEE Communications Letters, 6, 256-258. [7] Zhao, P.M. (2012) Thinking Python Like Computer Scientists. People’s Posts and Telecommunications Press, Beijing. [8] Chen, J. (2011) MATLAB Book. Electronic Industry Press, Beijing.