联合低秩及稀疏结构特性的毫米波通信下行信道估计

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。

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

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.