基扩展模型联合反馈DFT信道估计算法
The Channel Estimation Algorithm with Basis Expansion Model Combined Feedback Packet DFT
摘要:  为了提高快速移OFDM系统的信道估计的精度,进一步抑制载波间干扰(ICI),本文提出了一种基扩展模型(BEM)联合反馈分组DFT的信道估计算法(BEM + DFT)。首先,利用BEM算法估计出快速移动的信道信息和载波间干扰,然后,利用分组DFT算法进行二次信道估计,并对ICI及其他干扰进行二次消除,从而更加精确的估计出信道且进一步提升系统性能。仿真结果表明:本文建议的GCE-BEM + DFTKL-BEM + DFT算法性能相对于分组DFT算法、GCE-BEMKL-BEM性能有了明显的提升。
Abstract: To improve the fast-moving OFDM system channel estimation accuracy and further inhibit inter-carrier interference (ICI), we propose a channel estimation algorithm (BEM + DFT) with basis expansion model (BEM) combined feedback packet DFT. First, the BEM algorithm is used to estimate the fast moving channel information and the inter-carrier interference. And then, the packet DFT algorithm is used to further estimate the channel information and eliminate the ICI interference, etc. So the proposed algorithm can achieve more accurate estimate of the channel, and further enhancement in the performance of the system. Simulation results show that the proposed GCE-BEM + DFT and KL-BEM + DFT algorithm performance has been significantly improved compared to the grouping DFT algorithm, GCE-BEM and KL-BEM algorithm.
文章引用:漆安廷, 刘顺兰. 基扩展模型联合反馈DFT信道估计算法[J]. 无线通信, 2013, 3(6): 144-148. http://dx.doi.org/10.12677/HJWC.2013.36023

参考文献

[1] Yoshida, K. and Abiko, S. (2002) Inertia parameter identification for a free-flying space robot. AIAA Guidance, Navigation, and Control Conference and Exhibit, Monterey, 5-8 August 2002, 1.
[2] Li, D., Feng, S. and Zhuang, H. (2009) Correlative coding based channel estimation for practical OFDM systems over time varying channels. 5th International Conference on Wireless Com- munications, Networking and Mobile Computing, Beijing, 24-26 September 2009, 1-4.
[3] Tsatsanis, M.K. and Giannakis, G.B. (1996) Modelling and equalization of rapidly fading channels. International Journal of Adaptive Control and Signal Processing, 10, 159-176.
[4] Leus, G. (2004) On the estimation of rapidly time-varying channels. Proceedings of EUSIPCO, EUSIPCO, Vienna, 2227- 2230.
[5] Bhatti, J. and Moeneclaey, M. (2007) Pilot-aided carrier syn- chronization using an approximate DCT-based phase noise model. IEEE International Symposium on Signal Processing and In- formation Technology, Giza, 15-18 December 2007, 1143-1148.
[6] Visintin, M. (1996) Karhunen-Loeve expansion of a fast Rayleigh fading process. Electronics Letters, 32, 1712.
[7] Zemen, T. and Mecklenbrauker, C.F. (2005) Time-variant chan- nel estimation using discrete prolate spheroidal sequences. IEEE Transactions on Signal Processing, 53, 3597-3607.
[8] Tang, Z., Cannizzaro, R.C., Leus, G., et al. (2007) Pilot-assisted time-varying channel estimation for OFDM systems. IEEE Transactions on Signal Processing, 55, 2226-2238.
[9] Liu, C.H. and Chuang, G.C.H. (2011) Joint ICI cancellation and channel estimation with real-time channel adaptation for high- mobility OFDM systems. 2011 IEEE GLOBECOM Workshops, Houston, 5-9 December 2011, 1376-1381.
[10] Qiao, Y., Yu, S., Su, P., et al. (2005) Research on an iterative algorithm of LS channel estimation in MIMO OFDM systems. IEEE Transactions on Broadcasting, 51, 149-153.
[11] Xiao, C., Zheng, Y.R. and Beaulieu, N.C. (2003) Statistical simulation models for Rayleigh and Rician fading. IEEE In- ternational Conference on Communications, 5, 3524-3529.
[12] Kannu, A.R. and Schniter, P. (2005) MSE-optimal training for linear time-varying channels. IEEE International Conference on Acoustics, Speech, and Signal Processing, 3, iii/789-iii/792.