## 2 Channel Modeling for MIMO Wireless Orientations
With respect to Figure 3, after selecting the appropriate scenario and propagation conditions (line of sight-LOS or NLOS), the generation of final channel coefficients is based on intermediate steps computational calculations: correlated large-scale parameters, angles of arrival and departure, cross polarization ratio (XPR), cluster powers, and corresponding delays. Therefore, all aforementioned procedures can be easily implemented as separate functions during 5G network simulations. The 3 GPP channel model considers spatial consistency, blockage effects, and atmosphere attenuation at mmWave. Although this is a computationally efficient approach for 5G channel modeling, the model has limited capabilities of simulating dual-mobility, antenna array non-stationarity, and spherical waves in massive MIMO. The authors in [28] propose a novel approach to extend Winner-type GSCM to include all the aforementioned issues. In particular, the studied extension proposes to share some of the clusters between users that are near to each other to achieve the desired MU consistency. The number of shared clusters is related to the distance between users, but which clusters are shared can vary according to the implementation. Figure 2: MIMO (multiple input multiple output) cluster channel modeling for wireless cellular networks. In the NYUSIM simulator [36], various measurements for 5G and 6G mmWave channel models have been developed from 2 to 73 GHz. In this framework, a complete statistical channel model is provided along with simulation code (in Matlab) for the generation of realistic spatial and temporal wideband channel impulse responses. These results are available for public use and suitable in 3 GPP and other standard bodies and academic/industrial simulations. In this framework, in [37], an extended NYUSIM channel model with spatial consistency, human blockage and outdoor-to-indoor (O2I) penetration loss was developed. The multiple reflection surfaces method was adopted to update small-scale parameters in the spatial consistency procedure. The work in [38] presents a 3-D statistical channel impulse response (IR) model for urban LOS and non-LOS channels developed from 28- and 73-GHz ultrawideband propagation measurements in New York City. In [39], the characteristics of a spatial channel model for several mmWave bands (28, 38, 60, and 73 GHz), with the help of NYUSIM, are simulated. Finally, additional channel configurations in the framework of 5G orientations have been studied in literature as well. In [40], the authors provide an overview of the current state of research on Device to Device (D2D) channels and discuss future trends and research directions. In [41], a simulation study is provided regarding the feasibility of public safety UAV connectivity through a 5G link at 28 GHz. As the results indicate, 5G mmWave communications can provide throughput up to 1 Gbps with consistent sub ms latency when the BS is located near the mission area, enabling remote offloading of the UAV control and perception algorithms. In [42], the authors provide a tutorial on UAV communications in 5G-and-beyond wireless networks. In this framework, new considerations, such as UAV energy limitation, high altitude, and high 3D mobility, are discussed as well. The state-of-the-art results are examined for the two main research and application paradigms of UAV communications, namely UAV-assisted terrestrial communications and cellular-connected UAVs. In [43], the statistical behavior of the path-loss from a cellular BS toward a flying UAV is modeled. The modeling principle is based on the depression angle that influences the excess in the path-loss due to two contradicting effects, the first of which is the enhanced LoS condition of the UAV and the second is the reduced BS antenna gain due to the down-tilt of the antenna pattern. Figure 3: Channel modeling for wireless 5G cellular networks.