Improved joint antenna selection and user scheduling for massive MIMO systems

Massive multi-input multi-output (MIMO) technology is promising by employing a large number of antennas at the base station to support a large amount of users. However, due to the limitation of analog front-ends at the base station, the antenna selection and user scheduling strategies are essential to achieve spatial diversity and reduce hardware cost at the same time. In this work, we consider the strategy of joint antenna selection and user scheduling (JASUS) for uplink massive MIMO systems and propose a greedy two-step JASUS algorithm referred to as largest minimum singular value based JASUS (LMSV-JASUS). In its first step, a simplified downward branch and bound based JASUS is used to find a near-optimal antenna and user sets whose channel matrix has the near-largest MSV. In its second step, a swapping-based algorithm is proposed to find a better solution by swapping antennas and users between the selected and the discarded. The numerical results suggest that the proposed algorithm outperforms traditional approaches in terms of system sum-rate and computational complexity.