摘要
在多输入多输出通信系统中,接收端信道均衡与相干检测需要利用信道状态信息.传统信道估计方法如最小二乘法和最小均方误差法均基于多径信道密集型假设,导致频谱利用率低下.为此,该文研究在单载波MIMO系统中的稀疏信道估计,利用多径信道的稀疏特性提出一种基于压缩感知理论的信道估计方法.这种方法能利用较少的导频信号达到与传统方法相比拟的估计性能,从而提高频谱利用率.仿真验证和理论分析表明,基于压缩采样匹配追踪的压缩感知信道估计方法为MIMO系统稀疏信道估计的最优选择.
In MIMO systems,channel state information（CSI） is necessary for coherent detection and channel equalization at the receiver.Traditional channel estimation methods such as least squares（LS） and minimum mean square error（MMSE） are based on the rich multipath assumption which leads to low frequency spectrum utilization.This paper studies sparse channel estimation for single carrier MIMO systems.A novel method based on compressed sensing is proposed by using sparsity,which can obtain accurate CSI with fewer pilots so as to improve frequency spectrum utilization.Simulation and theoretical analysis show that the compressive sampling matching pursuit（CoSaMP） estimation is the best choice for MIMO sparse multipath channel estimation.
出处
《应用科学学报》
EI
CAS
CSCD
北大核心
2011年第4期347-352,共6页
Journal of Applied Sciences
基金
国家科技重大专项基金(No.2009ZX03002-009-01)资助
关键词
压缩感知
稀疏多径
信道估计
CoSaMP算法
多输入多输出
compressed sensing（CS）
sparse multipath
channel estimation
CoSAMP algorithm
multiple-input multiple-output（MIMO）
