have recently been conducted to investigate the user scheduling algorithm for various massive MIMO systems [12]. In [13], a semi-orthogonal user selection (SUS) algorithm was proposed for zero-forcing (ZF) precoding. In [14], a signal-to-interference-ratio-based user scheduling (SIRUS) method was addressed for matched filter (MF) precoding in downlink MIMO systems. For uplink massive MIMO, a multi-user grouping-based scheduling algorithm was investigated in [15], while a joint user scheduling and beam selection scheme was studied in [16] for beam-based massive MIMO systems. In [17], a greedy user selection algorithm for distributed massive MIMO was investigated. The aforementioned studies focused on reducing the complexity of optimal user scheduling, in which the complexity increased exponentially with the number of served users due to the exhaustive search. Therefore, this study investigates whether user scheduling is necessary for massive MIMO systems. Many previous studies [7; 18; 19; 20] have analyzed the performance of massive MIMO systems in terms of the ergodic sum rate. The optimality of MF was proven in [7] under the framework of non-cooperative multicellular networks. In [18], the deterministic equivalent forms of signal-to-interference-plus-noise ratio (SINR) for ZF and minimum mean-square error (MMSE) precoders were derived. In [19], the authors derived the asymptotic achievable rates of MF and MMSE precoders/receivers while considering pilot contamination. The effect of channel aging was analyzed in [20] using a similar analysis technique as that given in [18] and [19]. The previous works in [7; 18; 19; 20] have studied the performance of massive MIMO systems regarding the first moment of mutual information. However, because the diversity gain from user scheduling depends on natural or artificially induced fluctuations in the channel, it is important to estimate the fluctuations that can be expected in a particular system environment to investigate the feasibility of user scheduling. Therefore, in this paper, the performance of a massive MIMO system is analyzed in terms of the second moment of mutual information (i.e., the variance of mutual information) to understand how the fluctuations in mutual information can be varied according to the number of antennas. In [21; 22; 23], a phenomenon of massive MIMO systems, referred to as the channel hardening effect, was investigated. The channel hardening effect implies that the variance of mutual information shrinks as the number of antennas increases. In [21], the authors used Gaussian approximations to derive the distribution of capacity and discussed the implications of channel hardening for scheduling and rate feedback. The channel hardening phenomenon was observed in [22] when selecting an optimum antenna. The work in [22] was expanded to the case of multiple antenna selection in terms of energy efficiency in [23]. However, in the conventional works in [21; 22; 23], the channel hardening phenomena were studied in terms of capacity, without considering any practical signal processing techniques, such as linear precoders/receivers. In addition, in previous works [21; 22; 23], there were no comprehensive closed-form expressions of the variance of mutual information as a function of the number of antennas to scale the variance according to the number of antennas. Consequently, in this paper, we investigate the scaling laws of scheduling gain for uplink multiuser massive MIMO systems in order to verify the feasibility of user scheduling for massive MIMO, assuming MF and ZF receivers at the BS for data demodulation. First, we derive the exact probability density function (PDF) of SINR and its moment-generating function (MGF) to obtain the first and second moments of SINR under the perfect channel state information (CSI) at the BS. Using Taylor series expansion, we obtain a closed-form expression for the approximated variance of the individual rate for a user as a function of the number of BS antennas.