基于卡尔曼滤波的快时变稀疏信道估计新技术

袁伟娜; 王嘉璇

doi:10.3969/j.issn.0258-2724.2018.04.023

基于卡尔曼滤波的快时变稀疏信道估计新技术

doi: 10.3969/j.issn.0258-2724.2018.04.023

基金项目:

中央高校基本科研业务费专项基金资助项目 222201714032

国家自然科学基金资助项目 61501187

详细信息

作者简介:
袁伟娜(1979-), 女, 副教授, 博士, 研究方向为无线通信, E-mail:wnyuan@ecust.edu.cn

中图分类号: TN929.5
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- 文章访问数: 425
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- 被引次数: 0
出版历程
- 收稿日期: 2017-12-11
- 刊出日期: 2018-08-01

Fast Time-Varying Sparse Channel Estimation Based on Kalman Filter

摘要

摘要: 针对高铁以及山区环境下正交频分复用（orthogonal frequency division multiplexing，OFDM）通信系统的信道估计问题，提出一种基于卡尔曼滤波的快时变稀疏信道估计方法.该方法基于快时变信道的基扩展模型（basic expansion model，BEM），应用压缩感知（compressed sensing，CS）理论进行稀疏时延估计，并应用卡尔曼滤波（Kalman filter，KF）技术对BEM系数进行估计，进而获得信道增益.仿真结果表明，在相同信噪比（signal to noise ratio，SNR）条件下，随着归一化多普勒频移（frequency-normalized Doppler shift，FND）增大，新方法的信道估计均方差（mean square error，MSE）性能优于传统方法，如当SNR为20 dB，FND为0.1时，新方法较传统方法性能提升了4 dB，表明对信道时变性具有更优的鲁棒性；在相同的多普勒频移条件下，随着SNR增加，各方法的均方差均有所改善，新方法改善更明显，如当FND为0.2时，在信道估计均方差为0.06的条件下，新方法较传统方法获得了6 dB的信噪比增益，表明对抗信道噪声能力更强.
- 信道估计 /
- 基扩展模型 /
- 卡尔曼滤波 /
- 压缩感知
Abstract: A fast time-varying sparse channel estimation method based on the Kalman filter is proposed for channel estimation of an orthogonal frequency division multiplexing communication system operating in high-speed railways and mountain areas. Based on the basic expansion model (BEM), compressed sensing (CS) was employed for the estimation of sparse delays, and a Kalman filter (KF) estimator was utilised for estimating the BEM coefficients. The channel gains were then computed easily. The simulation results show that under the same signal-to-ratio (SNR) condition, with the increase in frequency-normalised Doppler shift (FND), the MSE of the new method is superior to that of traditional methods, such as SNR is 20 dB and FND is 0.1, and a 4 dB performance improvement is achieved. Under the same Doppler shift condition, the same result is obtained as that with the increase in SNR, such as FND is 0.2 and MSE is 0.06, and a 6 dB SNR gain is achieved. These results show that the new method is more robust to variation in channel time and stronger against noise compared with traditional methods.
- channel estimation /
- basic expansion model /
- Kalman filter /
- compressed sensing

HTML全文

图 1 SNR为20 dB, f_nd增大时, 各方法的NMSE对比

Figure 1. NMSE vs. f_nd for SNR is 20 dB

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图 2 f_nd=0.2, SNR增大时, 各方法的NMSE对比

Figure 2. NMSE vs. SNR for f_nd=0.2

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表 1 仿真参数表

Table 1. Simulation parameters

子载
波数 CP
长度导频
长度采样
间隔/μs 载频
/GHz 总径
数主径
数

128 16 32 2.5 2 20 5

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参考文献(17)

WU J, FAN P. A survey on high mobility wireless communications:challenges, opportunities and solutions[J]. IEEE Access, 2016, 4(1):450-476. http://ieeexplore.ieee.org/document/7383229/

FAN Pingzhi, ERDAL P. Guest editorial:special issue on high mobility wireless communications[J]. Journal of Modern Transportation, 2012, 20(4):197-198. doi: 10.1007/BF03325798

周杲, 范平志, 郝莉.基于OFDM的DFT加扰矢量码分多址接入技术[J].西南交通大学学报, 2017, 52(1):148-155. doi: 10.3969/j.issn.0258-2724.2017.01.021

ZHOU Gao, FAN Pingzhi, HAO Li. 0FDM based DFT scrambling vector code division multiple access[J]. Joumal of Southwest Jiaotong University, 2017, 52(1):148-155. doi: 10.3969/j.issn.0258-2724.2017.01.021

ROOZBEH M, ARASH A, Compressive sensing-based pilot design for sparce channel estimation in OFDM systems[J]. IEEE Communications Letters, 2017, 21(1):4-7. doi: 10.1109/LCOMM.2016.2613086

LEE D. MIMO OFDM channel estimation via bock stagewise orthogonal mnatching pursuit[J]. IEEE Communications Letters, 2016, 20(10):2115-2118. doi: 10.1109/LCOMM.2016.2594059

ROOZBEH M, ARASH A. Determinitic pilot design for sparce channel estimation in MISO/multi-user OFDM systems[J]. IEEE Transactions on Wireless Communications, 2017, 16(1):129-140. doi: 10.1109/TWC.2016.2619699

叶新荣, 朱卫平, 张爱清, 等. OFDM系统双选择性慢衰落信道的压缩感知估计[J].电子与信息学报, 2015, 37(1):169-174. http://d.old.wanfangdata.com.cn/Periodical/dzkxxk201501026

YE Xinrong, ZHU Weiping, ZHANG Aiqing, et al. Compressed sensing based on doubly-selective slow-fading channel estimation in ofdm systems cbannel estimation[J]. Journal of electronics and information, 2015, 37(1):169-174. http://d.old.wanfangdata.com.cn/Periodical/dzkxxk201501026

TAN Guoping, HERFET T. A framework of analyzing OMP-based channel estimations in mobile OFDM systems[J]. IEEE Wireless Communications Letters, 2016, 5(4):408-411. doi: 10.1109/LWC.2016.2571688

MA Xu, YANG Fang, LIU Sicong, et al. Structured compressive sensing-based channel estimation for time frequency training OFDM systems over doubly selective channel[J]. IEEE Wireless Communications Letters, 2017, 6(2):266-269. doi: 10.1109/LWC.2017.2669974

CHEN B, CUI Q, YANG F, et al. A novel channel estimation method based on Kalman filter compressed sensing for time-varying OFDM system[C]//International Conference on Wireless Communications & Signal Processing.[S.l.]: IEEE, 2014: 1-5.

RABBI M F, HOU S W, KO C C. High mobility orthogonal frequency division multiple access channel estimation using basis expansion model[J]. IET Communications, 2010, 4(3):353-367. http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0211112117/

SHENG Zhichao, TUAN H D. Pilot optimization for estimation of high-mobility OFDM channels[J]. IEEE Transactions on Vehicular Technology, 2017, 66(10):8795-8806. doi: 10.1109/TVT.2017.2694821

HIJAZI H, ROS L. Joint data QR-detection and Kalman estimation for OFDM time-varying Rayleigh channel complex gains[J]. IEEE Transactions on Communications, 2010, 58(1):170-178. doi: 10.1109/TCOMM.2010.01.080296

BERGER C R, ZHOU S, PREISIG J C, et al. Sparse channel estimation for multicarrier underwater acoustic communication:from subspace method to compressed sensing[J]. IEEE Transactions on Signal Process, 2010, 58(3):1708-1721. doi: 10.1109/TSP.2009.2038424

JAKES W C, COX D C. Microwave mobile communications[M].[S.l.]: IEEE Press, 1974: 28-46.

BADDOUR K E, BEAULIEU N C. Autoregressive modeling for fading channel simulation[J]. IEEE Transactions on Wireless Communications, 2005, 4(4):1650-1662. doi: 10.1109/TWC.2005.850327

QI F, JU Y, SUN S, et al. BEM-based reconstruction of time-varying sparse channel in OFDM systems[C]//Vehicular Technology Conference.[S.l.]: IEEE, 2013: 1-5.

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