Massive MIMO Codebook Design Using Gaussian Mixture Model Based Clustering

The codebook design is the most essential core technique in constrained feedback massive multi-input multi-output (MIMO) system communications. MIMO vectors have been generally isotropic or evenly distributed traditional designs. In this paper, Gaussian mixture model (GMM) based clustering proposed, which inspired by strong classification and analytical abilities of techniques. Huge quantities channel state information (CSI) are initially saved as entry data process. Further, split into N number clusters on shortest distance. centroids part has utilized for constructing a with statistic information, an average distance that towards true data. enhanced GMM outperforms methods, particularly situations non-uniform distribution channels demonstrated via simulation results match theoretical analyses concerning achievable rate. proposed compared DFT-based k-means design.