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作 者:郑娟毅 杨朴真 ZHENG Juan-yi;YANG Pu-zhen(School of Communication and Information Engineering,Xi’an University of Posts and Telecommunications,Xi’an 710121,China)
机构地区:[1]西安邮电大学通信与信息工程学院,陕西西安710121
出 处:《中国电子科学研究院学报》2024年第7期579-587,共9页Journal of China Academy of Electronics and Information Technology
基 金:国家自然科学基金资助项目(62271391)。
摘 要:在可重构智能表面(Recongigurable Intelligent Surface,RIS)辅助毫米波多输入多输出(Multiple Input Multiple Output,MIMO)系统中,针对毫米波引入的信道矩阵稀疏性,以及RIS引入的高维度信道矩阵,提出一种基于压缩感知和深度学习相结合的信道估计算法。首先,将少量RIS元件与射频链路链接,构建部分有源RIS元件辅助毫米波MIMO通信系统;其次,利用不同子载波之间的角域公共稀疏性,使用稀疏度自适应匹配(Sparse Adaptive Matching Pursuit,SAMP)算法重构信道矩阵,并将其视为二维含噪图像;最后,使用对称双路去噪网络(Symmetric Two-Way Denoising Network,STDN)对二维含噪图像去噪。其中,STDN由两条并行对称路径组成,可在不同尺度下提取特征,去除图像噪声,并将两路特征图整合,输出去噪信道矩阵。仿真结果表明,文中所提SAMP-STDN算法提高了信道估计精度。In the reconfigurable smart surface(RIS)assisted millimeter-wave multiple-input multipleoutput(MIMO)system,a channel estimation algorithm based on compressed sensing and deep learning is proposed for the sparsity of channel matrix introduced by millimeter-wave and the high-dimensional channel matrix introduced by RIS.Firstly,a small number of RIS components are linked to the RF link to construct a partial active RIS component-assisted millimeter-wave MIMO communication system.Secondly,using the common sparsity of the angular domain between different subcarriers,the channel matrix is reconstructed by the sparsity adaptive matching(SAMP)algorithm and regarded as a two-dimensional noisy image.Finally,the symmetric two-way denoising network(STDN)is used to denoise the two-dimensional noisy image.Among them,STDN is composed of two parallel symmetric paths,which can extract features at different scales,remove image noise,and integrate the two feature maps to output the denoising channel matrix.The simulation results show that the proposed SAMP-STDN algorithm improves the channel estimation accuracy.
分 类 号:TN928[电子电信—通信与信息系统]
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