Fast Beam Training for Extremely Large-Scale MIMO Based on Geometric Beam Patterns

In this paper, a fast beam training method based on geometric beam patterns is proposed. It accumulates precise geometric patterns created by extremely large-scale MIMO with displacement from different training frames and covers all directions. By analyzing beam gains of different patterns which contributed by the channel angle and geometric shapes, the proposed method acquires more channel angle information and significantly reducing beam training frames. Both analytical and simulation results demonstrate the proposed method has a faster error convergence speed compared with traditional methods. It becomes possible to achieve fast beam alignment in a few training frames for millimeter-wave extremely large-scale MIMO in high-mobility scenarios such as vehicular networks.