on the same order as that of the conventional RUS scheme. Furthermore, since our user selection schemes are independent of the SSF CSI of candidate-UEs, we no longer have to carry out channel estimation of all the candidate-UEs for user selection. Instead, only the active-UEs need to send pilots at each coherence slot. As a beneficial result, we are capable of saving the cost of channel training significantly and attaining more resources for data transmission.

It is worth pointing out that a location-adaptive transmission strategy was proposed for TDD based massive MIMO systems in [@huh_phd_2012]. Our work differs from [@huh_phd_2012] in several respects. Random locations of UEs are assumed in this paper, whereas in [@huh_phd_2012] the UEs were assumed to be placed at fixed points. Therefore, the optimal number of active-UEs in our paper is independent of specific channel realizations, while in [@huh_phd_2012] this number has to be re-calculated whenever any candidate-UE’s large-scale-fading (LSF) CSI changes. Moreover, we consider spatial correlation in the channel model, which is ignored in [@huh_phd_2012].

The remainder of this paper is organized as follows. The system model is presented in Section II. In Section III, we analyze the asymptotic sum rates of the system invoking 