# Clustering-Based Nonstationary Massive MIMO Channel Modeling at 1.4725 GHz
## 1 Introduction  
Massive multiple input and multiple output (Massive MIMO), as a key technique for the 5 th generation (5G) wireless communications systems, presents a highly promising solution to meet the demanding requirements of spectrum efficiency and the energy efficiency [1, 2, 3]. The full capacity gain is mostly based on the assumption that asymptotic orthogonality of the subchannels, which requires that the channel fading coefficients satisfy independent and identical distribution. However, huge numbers of element antennas have proven to be double-edged, a great opportunity but also challenging the next generation of communication systems. New propagation characteristics are inevitably brought into wireless channel modeling due to the dense deployment of so many antenna elements. Both element number and physical size of the antenna array lead to the sub-channel correlation, which deeply influences the cooperative work of the Massive MIMO antenna element [4]. In recent years, research has spotlighted the state-of-the-art channel models. Among them, non-stationary modelling draws more attention. Lund University conducted measurements in 2.6 GHz, and the received power showed some discontinuous jump points in the dimensions of the antenna array, indicating the inconsistency of the elements [5]. In the outdoor macro-cell, three-dimensional spatial parameters of azimuth angle and elevation angle are extracted based on the realistic field data, and from the viewpoint of scatterer clusters, the correlation between clusters is fully proven [6]. In high-speed railway channel modeling, nonstationarity, as a significant requirement, is pointed out, and an angular spread-dependent model is proposed to describe the relationship between the angular dispersion and the spatial correlation [7, 8]. Then, in the outdoor measurement of 64-array antenna in 3.3 GHz, the results of the angle power spectrum and the power delay profile show that the channel does not meet the stationary condition in the spatialdomain nor in the frequency domain [9]. In another measurement, with 128 antenna arrays, the non-stationary characteristic of the channel is further proven due to the influence of multipath distribution [10]. Then, in a series of measurements at 2, 4 and 6 GHz bandwidth, the non-stationary characteristic of the channel is also confirmed by the correlation bandwidth [11]. Some works cast light on visible clusters and visible regions, based on which a novel Massive MIMO model is proposed. Even more, the contribution of non-stationarity to the sum and rate of upstream channels is proved by numerical analysis [12]. In these measurements most multipaths behave in clusters. Here, a cluster is considered as a set of multipath components with similar power, delay and spatial characteristics. So far, many traditional models have been based on clusters, such as Saleh-Valenzuela [13], COST2100 [14], 3 GPP SCM [15] and WINNER [16]. The traditional geometric model is mathematical based, which would go some way to weaken the universality. The dependence of the channel parameters on the measurement data result in that the channel model can only be applied to the cases with high statistical similarity with the measurement environment. Once the changes happen, the original model will degrade or even fail to work. Therefore, a multi-path clustering algorithm based on big data technology is introduced into the Massive MIMO wireless channel modeling with the development of information technology, which can help us to determine the random channel parameters through model training, which means, based on the real measurement data, we can quickly and effectively construct the channel model. It is an inevitable trend that statistics-based big data technology is widely accepted in wireless channel modeling. With the enlargement of the antenna array scale, the improvement in channel bandwidth, the subdivision of transmission scenarios and the expansion of information scale, the in-field data volume shows an \"exponential growth\".