摘要
传统的LS算法、MMSE算法应用于信道估计时需要进行协方差矩阵求逆的运算,当信号数量庞大时,会有很高的计算复杂度。考虑到信号稀疏性的特点,可将压缩感知理论应用于信道估计中。常见的压缩感知贪婪类算法有OMP算法和CoSaMP算法,这两种算法需要将稀疏度作为已知条件,因此限制了其使用。提出基于降噪回溯SAMP算法(NrSAMP),将其应用于Massive MIMO系统的信道估计。该算法改进了传统SAMP算法在信道估计时的步长选择,避免了稀疏度已知的必要条件,同时利用降噪技术,在噪声环境下获得了更好的重构精度,证明了该算法的优势和较高的使用价值。
When traditional LS algorithm and MMSE algorithm are applied to channel estimation,the covariance matrix inversion is needed.When the number of signals is large,the computation complexity will be very high.Considering the sparsity of signals,compressed sensing theory can be applied to channel estimation.The common greedy algorithms of compressed sensing include OMP algorithm and CoSaMP algorithm,which need to take sparsity as a known condition,so their use is limited.This paper proposes a noise reduction backtracking SAMP(NrSAMP)algorithm,which is applied to channel estimation in Massive MIMO system.It improves the step size selection of traditional SAMP algorithm in channel estimation,avoids the necessary condition of known sparsity,and achieves better reconstruction accuracy under noise environment by using noise reduction technology,which proves its superiority and higher application value.
作者
徐昊
李春树
Xu Hao;Li Chunshu(School of Physics and Electronics-Electrical Engineering,Ningxia University,Yinchuan 750021,Ningxia,China;Ningxia Key Laboratory of Intelligent Sensing for Desert Information,Yinchuan 750021,Ningxia,China)
出处
《计算机应用与软件》
北大核心
2021年第1期99-104,共6页
Computer Applications and Software
基金
宁夏自然科学基金项目(NZ17051)。
