For millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, hybrid processing architecture is usually used to reduce the complexity and cost, which poses a very challenging issue in channel estimation. In this paper, deep convolutional neural network (CNN) is employed to address this problem. We first propose a spatial-frequency CNN (SF-CNN) based channel estimation exploiting both the spatial and frequency correlation, where the corrupted channel matrices at adjacent subcarriers are input into the CNN simultaneously. Then, exploiting the temporal correlation in time-varying channels, a spatial-frequency-temporal CNN (SFT-CNN) based approach is developed to further improve the accuracy. Moreover, we design a spatial pilot-reduced CNN (SPR-CNN) to save spatial pilot overhead for channel estimation, where channels in several successive coherence intervals are grouped and estimated by a channel estimation unit with memory. Numerical results show that the proposed SF-CNN and SFT-CNN based approaches outperform the non-ideal minimum mean-squared error (MMSE) estimator but with reduced complexity, and achieve the performance close to the ideal MMSE estimator that is very difficult to be implemented in practical situations. They are also robust to different propagation scenarios. The SPR-CNN based approach achieves comparable performance to SF-CNN and SFT-CNN based approaches while only requires about one third of spatial pilot overhead at the cost of complexity. Our work clearly shows that deep CNN can efficiently exploit channel correlation to improve the estimation performance for mmWave massive MIMO systems.
翻译:对于毫米波(mmWave)大规模多输出多输出(MIMO)系统,通常使用混合处理结构来降低复杂度和成本,这在频道估算方面是一个极具挑战性的问题。在本论文中,利用深层进化神经网络(CNN)来解决这一问题。我们首先建议使用基于空间频率CNN(SF-CNN)的频道估算,利用空间频率和频率的相互关系,将相邻的子容器的腐败通道矩阵同时输入CNN。然后,利用时间变化频道的时间相关性相关性,开发基于空间频率-时空CNN(SFT-CNN)的方法,进一步提高频道估算的准确性。此外,我们设计了一个空间试点式的CNNN(SPR-CNN)网络网络(SPR-CNN) 来节省频道估算的空间试点间接成本,将几个相继接续的频道的频道组合起来,由具有记忆的频道估测单位估算。NFT-N和SFT-N(SF-N) 基础的深度方法可以比近于非最低平均CNR(SM-R) 的系统,在Sestimal IMSestal(Mestal IM) 的运行的运行中,其运行中,其性能要求其精确到非常复杂的运行到最接近性能变化的运行,其性能变。