CN  Vol.5 No.3 C , September 2013
Estimation of Non-WSSUS Channel for OFDM Systems in High Speed Railway Environment Using Compressive Sensing
Abstract: Non Wide Sense Stationary Uncorrelated Scattering (Non-WSSUS) is one of characteristics for high-speed railway wireless channels. In this paper, estimation of Non-WSSUS Channel for OFDM Systems is considered by using Compressive Sensing (CS) method. Given sufficiently wide transmission bandwidth, wireless channels encountered here tend to exhibit a sparse multipath structure. Then a sparse Non-WSSUS channel estimation approach is proposed based on the delay-Doppler-spread function representation of the channel. This approach includes two steps. First, the delay-Doppler-spread function is estimated by the Compressive Sensing (CS) method utilizing the delay-Doppler basis. Then, the channel is tracked by a reduced order Kalman filter in the sparse delay-Doppler domain, and then estimated sequentially. Simulation results under LTE-R standard demonstrate that the proposed algorithm significantly improves the performance of channel estimation, comparing with the conventional Least Square (LS) and regular CS methods.
Cite this paper: Wang, C. , Fang, Y. and Sheng, Z. (2013) Estimation of Non-WSSUS Channel for OFDM Systems in High Speed Railway Environment Using Compressive Sensing. Communications and Network, 5, 661-665. doi: 10.4236/cn.2013.53B2118.

[1]   S. Chen, “China Unveils High-Speed Railways,” 2011.

[2]   D. Xin., “Record-Breaking Train on Track,” 2010.

[3]   L. Liu, et al., “Position-Based Modeling for Wireless Channel on High-Speed Railway under a Viaduct at 2.35 GHz,” IEEE Journal on Selected Areas in Communications, Vol. 30, No. 4, 2012, pp. 834-845.

[4]   T. Gao and B. Sun, “A High-Speed Railway Mobile Communication System Based on LTE,” Electronics and Information Engineering (ICEIE), 2010, Vol. 1, pp. 414-417.

[5]   K. Guan, Z. Zhong and B. Ai, “Assessment of LTE-R Using High Speed Railway Channel Model,” International Conference Communications and Mobile Computing (CMC), 2011, pp. 461-464.

[6]   F. Pena-Campos, et al., “Estimation of Fast Time-Varying Channels in OFDM Systems Using Two-Dimensional Prolate,” IEEE Transactions on Wireless Communications, Vol. 12, No. 2, 2013, pp. 989-907.

[7]   L. L. He, S. D. Ma, and Y. C. Wu, “Pilot-Aided IQ Imbalance Compensation for OFDM Systems Operating Over Doubly Selective Channels,” IEEE Trans on Signal Processing, Vol. 59, No. 5, 2011, pp. 2223-2233.

[8]   H. Hijazi, et al., “Channel Estimation for MIMO-OFDM Systems in Fast Time-Varying Environments,” 2010 4th International Symposium on Communications, Control and Signal Processing (ISCCSP), 2010, pp. 1-6.

[9]   W. C. Li, and C. P. James, “Estimation of Rapidly Time-Varying Sparse Channels,” IEEE Journal of Oceanic Engineering, Vol. 32, No. 4, 2007, pp. 927-939.

[10]   G. Matz, “On Non-WSSUS Wireless Fading Channels,” IEEE Transactions on Wireless Communications, Vol. 4, No. 5, 2005, pp. 2465-2478.

[11]   M. Jachan and M. Gerald, “Nonstationary Vector AR Modeling of Wireless Channels,” Workshop on Signal Processing Advances in Wireless Communications, 2005, pp. 625-629.

[12]   W. U. Bajwa, et al., “Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels,” Proceedings of the IEEE, Vol. 98, No. 6, 2010, pp. 1058-1076.

[13]   T. H. Eggen, B. B. Arthur and C. P. James, “Communication over Doppler Spread Channels. Part I: Channel and Receiver Presentation,” IEEE Journal of Oceanic Engineering, Vol. 25, No. 1, 2000, pp. 62-71.

[14]   Waheed Uz Zaman Bajwa, “New Information Processing theory and Methods for Exploiting Sparsity Inwireless SystemS,” Ph.D. Thesis, University Of Wisconsin-Madison, 2007.

[15]   W. U. Bajwa, A. M. Sayeed and R. Nowak. “Learning Sparse Doubly-Selective Channels,” 46th An-nual Allerton Conference on Communication, Control, and Computing, 2008, pp. 575-582.

[16]   N. Vaswani, “Kalman Filtered Compressed Sensing,” 15th IEEE International Conference on Image Processing, 2008. ICIP 2008, 2008, pp. 893-896.

[17]   N. Vaswani, “Analyzing Least Squares and Kalman Filtered Compressed Sensing,” IEEE International Conference on Acoustics, Speech and Signal Processing, 2009. ICASSP, 2009, pp. 3013-3016.