## 1 Introduction
According to the results, while bounds derived for NP detection shows performance gains for a fusion center with massive MIMO in low sensor transmit power scenarios, the benefit is shown to disappear when the transmit power is high. However, for the ED, having massive MIMO at the fusion center provides a significant advantage even when the sensors have high power. In [15], authors considered distributed detection in wireless sensor networks with a massive MIMO fusion center. Using the large deviation principle and random matrix theory, they analyzed the asymptotic detection performance of optimal hypothesis testing in terms of error exponents for the false alarm and miss detection probabilities which enable us to predict how difficult it will be to attain a certain level of detection reliability. Various studies already have shown that massive MIMO is quite effective to support distributed IoT devices. However, one of big obstacles to the realization of massive MIMO systems is reference signal (RS) overhead, which increases as the number of transmitter (TX) antennas and/or user entities (UEs) increases. The RS overhead reduction is a classical issue in research field of wireless communication, and numerous related technologies have been introduced [16; 17; 18; 19; 20]. In general, for massive MIMO, it is very difficult to apply orthogonal RS to each antenna due to the excessive amount of TX antennas, thus antenna group-based RS overhead reduction scheme is generally applied [20; 21]. In this kind of scheme, in each antenna group, the same RS is shared. In the case, there is enough correlation among antennas, antenna group can be decided based on beam groups and locations of distributed UEs. If there is not enough correlation in each antenna, several RS superposition schemes can be applied. A blind channel estimation scheme using the specific statistical property of the signal and the channel were introduced in [22; 23]. A training based super-imposed signal, which locates the RS stream and data stream in the same resources, and can also be applied to super-imposed RS streams, were introduced in [24; 25]. It is already proven that the antenna group-based RS overhead reduction scheme is quite effective. One of important factors in designing the antenna group-based RS overhead reduction scheme is how many antennas will be grouped in each group. Several techniques that can effectively be applied to antenna group-based RS overhead reduction scheme have been proposed [20; 21], while little work has been done for the selection criterion of the number of antennas needed in each group, and related system design [16; 17; 18; 19; 22; 23; 24; 25]. All of the work described above focuses solely on the methodology of how to reduce RS overhead. In real situation, if we assume we have the RS overhead reduction scheme at hand based on any kind of existing methodology, before applying the scheme, it is necessary to decide the target amount of RS overhead reduction in given situations. In addition, it is also necessary to design the systematic operational step and apparatus for real implementation. As far as we know, these have not been actively discussed in the literature yet. In this paper, we propose a simplified determination scheme of the number of antennas needed in each group for massive MIMO to support wireless sensor networks, and present related system structure for the operation. Basically, our contribution in this paper can be divided into two parts. First, we analyze the performance of RS overhead reduced massive MIMO systems with antenna grouping and related channel estimation error. Even though antenna grouping-based RS overhead reduction scheme is quite effective for the operation of massive MIMO and related IoT device support, channel estimation error is inevitable. The closed-form approximations of achievable SE, which includes related channel estimation error factor, are derived. Based on the channel estimation error factor, both RS overhead performance and the seriousness of channel estimation error can be adjusted.