| 摘要: |
| 针对时频双选信道，利用信道的时间相关性，即同一条时延径在相邻时刻对应的信道系数之间具有很强的相关性，提出一种线性近似方法对时频双选信道进行建模，有效降低了未知参数的个数.考虑到无线信道在时延域具有稀疏性，基于压缩感知（Compressed sensing，CS）理论对线性近似模型进行了恢复重构.分别对未线性近似模型和线性近似模型的系统性能进行了仿真，并结合最小二乘（leastsquare，LS）算法、正交匹配追踪（Orthogonal Matching Pursuit，OMP）算法、稀疏贝叶斯学习（Sparse Bayesian Learning，SBL）算法给出了系统的归一化均方误差（Normalized Mean Square Error，NMSE）曲线.仿真结果显示， 线性近似方法能有效对时频双选信道进行建模，针对本研究提出的线性近似模型，SBL算法能精确恢复出信道响应，并能有效地克服多谱勒效应. |
| 关键词: 时频双选信道 信道估计 压缩感知 稀疏贝叶斯学习 多谱勒效应 |
| DOI：10.3969/J.ISSN.1000-5137.2017.01.017 |
| 分类号: |
| 基金项目:国家自然科学基金（1471236，61420106008，61671295）；111计划（B07022）；上海浦江人才计划（16PJD029） |
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| Time-frequency doubly selective channel estimation based on compressed sensing |
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Tu Yuliang, Gui Lin, Qin Qibo, Luo Hanwen
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School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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| Abstract: |
| In this paper,considering time-frequency doubly selective channel,we utilize the channel's time correlation that the channel coefficientscorresponding tothe neighboring instants have a strong correlation.And we present a linear approximation method,which effectively reduces the number of unknown parameters.Considering the sparseness of the wireless channel in the delay domain,this paper reconstructs unknown channel parameters of the proposed model based on compressed sensing (CS) theory.In the simulations,we observe the system performance of the linear approximation model and the non-linear approximation model,respectively,and present the normalized mean squared error (NMSE) curves based on the least square (LS),orthogonal matching pursuit (OMP) and sparse Bayesian learning (SBL) algorithms.Simulation results show that the linear approximation method can effectively model the time-frequency doubly-selective channels.For the proposed linear approximation model,SBL algorithm can accurately recover the channel response,and overcome the Doppler effecteffectively. |
| Key words: time-frequency double selection channel channel estimation compressed sensing bayesian learning doppler effect |
