duplex (TDD) and frequency-division duplex (FDD) based massive MIMO systems impose different requirements on user selection. By exploiting the instantaneous CSI of candidate-UEs, Lee et al. proposed an SUS-like user selection method in [@lee_asympt_2014] and Xu et al. developed a greedy user selection scheme in [@xu_user_2014]. These selection methods mainly focus on FDD scenarios, in which the amount of downlink transmission resources consumed by the downlink channel estimation training for all the candidate-UEs does not increase with the number of candidate-UEs $N$[@kobayashi_training_2011]. By contrast, in TDD scenarios, the downlink channel is estimated through uplink training relying on channel reciprocity, and the pilot/training symbol overhead imposed by channel estimation increases with $N$[@marzetta_how_2006]. In this scenario, if the number of candidate-UEs is large, most of the channel coherence slot in time domain will be consumed by channel estimation, leaving only a small fraction for downlink data transmission. Hence, besides the high computational complexity, the pilot overhead needed for channel estimation of candidate-UEs also limits the application of instantaneous CSI-aided user selection methods in TDD scenarios. In addition, for FDD based massive MIMO systems, Nam et al. introduced user selection methods based on the candidate-UEs’ feedback of instantaneous signal-to-interference-plus-noise ratio (SINR) in[@nam_joint_2014]. However, 