Grouping Optimization Based Hybrid Beamforming for Multiuser MmWave Massive MIMO Systems

In millimeter-wave massive multiple input multiple output multiuser systems, inter-user interference becomes a major factor limiting system capacity. The premise of increasing system capacity is to minimize inter-user interference on the basis of ensuring large receiving power. In response to this situation, this paper proposes a low complexity grouping optimization based hybrid beamforming (HBF) algorithm. Specifically, we group users according to user channel correlation and a correlation threshold. Users with strong correlation are grouped into a group. Then, with the goal of maximizing capacity, the low-dimensional exhaustive algorithm is used in each group to select the base station beamforming vector. Moreover, a greedy algorithm is adopted, i.e., the influence of the beamforming vectors of the previous groups is considered. Simulation results show that the system sum rate of the grouping optimization HBF algorithm is higher than that of the existing HBF algorithms.