联合低秩及稀疏结构特性的毫米波通信下行信道估计  

Joint Low Rank and Sparsity-based Channel Estimation for FDD Massive MIMO

作　　者：周金[1] Zhou Jin(Tianjin University of Finance and Economy,School of Science and Technology,Tianjin 300222,China)

机构地区：[1]天津财经大学理工学院,天津300222

出　　处：《系统仿真学报》2021年第1期99-108,共10页Journal of System Simulation

基　　金：国家重点研发计划(2017YFC0806402);国家自然科学基金(61502331);2019年天津市智能制造专项基金(20191002);天津市自然科学基金(18JCYBJC85100);教育部人文社会科学研究规划基金(19YJA630046)。

摘　　要：毫米波通信的信道估计给系统带来较大负荷。为降低系统开销,联合无线信道低秩和稀疏特征,提出一种基于非凸低秩逼近的信道估计算法框架。针对基于信道建模的字典学习方法运算量大的问题,设计了基于深度神经网络信道特征分类的字典学习算法。仿真表明:在特定城市微蜂窝信道模型下,该方法的均方误差性能均优于基于信道模型的字典学习方法、贝叶斯框架下的信道估计方法以及基于压缩感知信道估计方法;获取相同归一化均方误差时本文算法所需的信噪比最低;所需导频数量低于上述3种方法。Channel estimation of millimeter wave communication needs large system load. In order to reduce the load, a low-rank and sparse feature of the wireless channel is combined, and a channel estimation algorithm framework based on non-convex low-rank approximation is proposed. Aiming at the large computation of the channel model-based dictionary learning algorithm, a dictionary learning algorithm for deep neural network channel feature classification is designed. The simulation shows that the average square error of the proposed method is better than the channel model-based dictionary learning method, the channel estimation method under the Bayesian framework, and the compressed sensing channel estimation method under the specific city microcellular channel model. The signal-to-noise ratio required by the algorithm is the lowest when the mean square error is the same. The number of pilots required is lower than the above three methods.

关 键 词：超大规模智能天线 非凸算法 深度神经网络 信道状态信息 字典学习 

分 类 号：TN929.5[电子电信—通信与信息系统] TP391[电子电信—信息与通信工程]