K-means clustering algorithm in antenna selection for Massive MIMO

Massive multiple-input multiple-output (MIMO) is considered as the promising technique in next generation of wireless communication system. It has high spectral efficiency and energy efficiency. In addition, it can mitigate the interferences among users as the amount of antennas at base station (BS) grows. But, the high complexity and cost of hardware pose huge challenges to massive MIMO. In future, antenna selection (AS) is still a choice to decrease the burden of BS. In this letter, the AS algorithm based on K-means clustering is proposed to maximizing the capacity with low computational complexity. Finally, the simulated results are presented to validate the proposed method. (paper)