| Summary: | Abstract 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.
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