## 2 Channel Modeling for MIMO Wireless Orientations  
Prior to the actual performance evaluation of a wireless cellular orientation, it is essential to have an accurate channel representation, able to represent all propagation mechanisms. With respect to Figure 1, network configuration may now vary according to traffic conditions and service demand. Therefore, the concept of fixed BS locations is no longer valid, since intermediate relay nodes can be placed throughout the 5G network topology in order to guarantee minimum Quality of Service (QoS) to MSs. In the same context, additional features, such as UAVs to support dynamic network configuration and device to device (D2D) communications impose an additional degree of complexity to the way we model channel conditions. In this framework, two main channel representations can be found in literature: Stochastic models, and deterministic ones. In the first case, these models employ ray tracing techniques in order to compute delay and other associated parameters, using geographical information [26; 27]. In the second case, these parameters are calculated based on parametric models using well-known probability density functions, by means of statistical analyses of measurement data collected during extensive measurement campaigns [28]. These models are also referred as Geometry-Based Stochastic Channel Models (GSCM). The existing GSCM can be further classified into two categories: the Winner type ones and the COST ones. For the first case, various examples can be found in literature, such as the 3 GPP spatial channel model (SCM), extended SCM (SCME) [29], Winner (WIM1), Winner II (WIM2) [30], Winner+ (WIM+), and QuaDRiGa [31]. Their main characteristic consists of the definition of the scatterers based on the angles of departure and angles of arrival, i.e., from a terminal perspective point of view. A typical use case is shown for example in Figure 2, where \(\theta_{n,m,Jo0}\), and \(\theta_{n,m,AoA}\) represent the angles of departure (AoD) and arrival (AoA), respectively, for the \(m^{\text{th}}\) subpath of the \(n^{\text{th}}\) cluster. On the other hand, COST-type GSCM [32] defines the physical position of the scatterers in the simulation area. Figure 1: A fifth generation (5G) network topology with dynamic network configuration. A comprehensive study for all channel models proposed and evaluated in the concept of 5G networks is described in [33]. In this context, the requirements for 5G accurate channel modeling are first outlined, and then an extensive review of the recent channel measurements and models is provided. Channel measurement campaigns for MIMO orientations, vehicle-to-vehicle (V2V), high-speed train (HST) and mmWave communications are described as well. The latest 3 GPP channel model (3 GPP TR38.901) [34] is the extension of the widely used 3 GPP 3D channel model with various additional modeling components. It supports a wide frequency range (0.5-100 GHz) and a large bandwidth (up to 10% of carrier frequency). In 3 GPP-5G modeling, a three-model representation of the MIMO channel is considered (Figure 2). In this context, the equivalent channel coefficient between an arbitrary pair of transmit/receive antenna is modeled as a set of individual channels from different clusters. In each cluster, the channel is further analyzed in a sum of \(M\) subpaths. Associated parameters, such as delay spread, angular spreads, Ricean K factor (K), shadow fading (SF), delays of multipath components, etc., are calculated with the help of random variable generation. However, appropriate parameter selection for the random variable distribution requires extensive measurement campaigns to be conducted, with multiple technologies present, e.g., mmWave massive MIMO channel measurements [35].