# Clustering-Based Nonstationary Massive MIMO Channel Modeling at 1.4725 GHz
## 1 Introduction
It is impossible to mine huge amounts of channel measurement data solely by human labor. Big data technology is a new technology that deals with massive and complex data and extracts valuable information quickly. With the development of large data technology, the application of mathematical algorithms on massive data to classify data types, analyze data regularity, and extract data relationships can help people explore correlations in complex data quickly and effectively. Machine learning, as a branch of artificial intelligence, is a key method of large data clustering. In recent years, the theory of artificial intelligence has been developed rapidly. The method of machine learning is helpful to enhance the adaptability of the channel model. Suppose that we have the framework of the model, the only work we need to do is to train the model with mass in-field data. Before that, we can develop the training criteria, such as MMSE (minimum mean square error). Good performance and practicability are important in channel modeling, which is meaningful to reduce the cost of re-modeling. Massive MIMO wireless channel modeling takes a step forward by applying the methods of big data and machine learning. Compared with the logical ways, such as deduction and reasoning, the method of big data pays more attention to interconnections of data instead of causal analysis. It is also different from the traditional modeling method using geometric statistics. The geometric mode is usually based on a certain physical framework, then the model parameters are estimated using a large number of measurement data. Big data technology is a serial operation of searching, comparison and clustering, aiming to reveal the inner connections among massive data sets. It makes the model more complex to take the spatial dimension into consideration during channel modeling. However, if we catch the key item of the correlation, which is verified to exist in entries of the channel matrix, it is possible to lower the dimensions. Usually we use statistical and theoretical deduction methods to determine model parameters, while the big data method estimates these variable parameters through model training. It means a large number of in-field measurement data are employed to train the model based on a given model framework. Then, the optimal parameter set can be calculated, and the model can be determined. The innovative application of big data technology in channel modeling has attracted wide attention in academia. The main contributions of this paper include realistic channel measurement, parameter estimate based on big data technology and state-of-the-art modelling. We focus on correlation and non-stationary characteristics. First, an account is given to describe the measurements campaign. The platform is equipped with a scalable virtual 128-element antenna array, the raw measurement data are recorded, and the channel impulse response (CIR) is illustrated. Next, a series of high-resolution estimation algorithms are employed. We carry out the data analysis based on big data technology. Then, a state-of-the-art channel model is presented based on the antenna correlations over the array. Last, we draw the conclusions. The paper is organized as follows: in Section 2 the Massive MIMO channel measurement campaign and primary signal processing are introduced. Then, in Section 3 the clustering algorithm is outlined, and the channel parameters extracted from the measured snapshots is demonstrated. We also establish the non-stationary Massive MIMO channel model. Finally, in Section 5, we summarize our contributions and draw conclusions.