In this paper, we propose a convolutional neural network (CNN) and clustering-based codebook design method. Specifically, we train two different CNNs, i.e., CNN1 and CNN2, to compress the channel state information (CSI) matrices into the channel vectors and recover the channel vectors back into the CSI matrices, respectively. After that, the clustering algorithm clusters the output of CNN1, i.e., the channel vectors into several clusters and outputs a centroid for each cluster. The sum distance between each centroid and the channel vectors in the corresponding cluster is the smallest, which can lead to the maximum sum rate of massive MIMO codebook design. Then, the centroids are recovered into matrices by CNN2. The output of CNN2 is our proposed codebook for massive multiple-input multiple-output (MIMO) systems. In the simulation, we compare the performance of different clustering algorithms. We also compare the proposed codebook with the traditional discrete Fourier transform (DFT) codebook. Simulation results show the superiority of the proposed algorithm.

在本文中，我们提出了一种卷积神经网络（CNN）和基于聚类的码本设计方法。具体来说，我们训练了两个不同的 CNN，即 CNN1 和 CNN2，分别将信道状态信息 (CSI) 矩阵压缩为信道向量，并将信道向量恢复回 CSI 矩阵。之后，聚类算法将 CNN1 的输出，即通道向量聚类成几个簇，并为每个簇输出一个质心。每个质心与对应簇中信道向量的和距离最小，这可以导致大规模 MIMO 码本设计的最大和率。然后，通过 CNN2 将质心恢复为矩阵。CNN2 的输出是我们为大规模多输入多输出 (MIMO) 系统提出的码本。在模拟中，我们比较了不同聚类算法的性能。我们还将提出的码本与传统的离散傅里叶变换 (DFT) 码本进行了比较。仿真结果表明了所提算法的优越性。