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: 464
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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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