Deep Clustering-Based Codebook Design for Massive MIMO Systems

Deep learning (DL) can effectively handle explosive data and solve the complicated non-convex problems. Recently, it is used to reduce the overhead of channel state information (CSI) feedback and improve the performance of the limited feedback massive MIMO systems. However, the complex wireless environment and the high-dimensional data of massive MIMO channel degrades the performance of the DL based codebook design. To address this issue, we propose a deep clustering (DC) based codebook design for massive MIMO systems. At first, deep neural network (DNN) learns the key propagation characteristics of the wireless channel according to the training model. And then, the clustering algorithm outputs the centroids of each key propagation characteristic, which can be used to reconstruct the valid space of massive MIMO channel. Finally, massive MIMO codebook is constructed by the valid space. Since the amount of the key propagation characteristics are far less than the dimension of massive MIMO channel, the proposed design can significantly simplify the parameters and the structures of DNN. Moreover, the clustering algorithm output the information with minimum sum-distance with the real channel sequences which can acquire the maximum sum-rate of massive MIMO codebook design. The robustness of the proposed codebook design is validated by carrying out extensive simulation in various scenarios and antenna array structures. Simulation results consist with theoretical analysis in terms of the achievable rate, and demonstrate that the proposed codebook design outperforms the traditional schemes.