## 1 Introduction
### State of the Art for Channel Modeling  
The literature presents many channel models, and generally two main approaches are taken into account: stochastic and statistical. The first is based on a step-by-step analysis of the probability density function (PDF) of the channel parameters which are based on the geometry of the considered scenario. Stochastic models are easy and quick to formulate, but they cannot be completely validated without field measurements data or through a ray-tracing analysis. Meanwhile, statistical models can be formulated after exhaustive measurements on the field and a successive formulation based on the data. The model obtained through this approach is limited for the region of the measurements, and the final data can suffer from a significant variation depending on the geographical location. Moreover, a statistical model can suffer from low accuracy related to the used instruments and the post-processing method. To overcome the drawbacks of these strategies, a hybrid approach is the most widely adopted, in which the channel parameters can be stochastically characterized by studying their probability distributions. Then, field measurements can be adopted to verify the first step and eventually modify the channel model [22]. Regardless of the adopted strategy, MIMO channel models for mmWave system level simulations mainly use the Clustered Delay Line (CDL) model in which the scatterers are grouped into clusters distributed over the area. Each cluster contains multipath elements with the same delay and small variations in angles of departure and arrival. A milestone for the channel modeling has been provided by the Information Society Technologies (IST) with the Wireless World Initiative New Radio (WINNER) project. In 2007, they released the deliverable WINNER II [23], as an evolution of the WINNER I project, providing channel models for link level and system level simulations in many scenarios such as local area, metropolitan area, and wide area. They offered a 2-D stochastic channel model approach which is geometry-based and antenna independent (i.e., different antenna configurations and element patterns can be used). They provide a hybrid approach, and the channel instances are generated by combining the outcomes of rays with particular channel parameters (power, delay, and angle of arrival and departure). WINNER II offers the capability to cover scenarios with both indoor and outdoor users, elevation in indoor applications, and _LOS_ and non-line-of-sight (_NLOS_) conditions, and the models are scalable from a SISO system to a MIMO system. The proposed channel modeling approach can be applicable to any wireless system operation in the 2-6 GHz frequency range with a variable bandwidth up to 100 MHz. In 2014, researchers presented the Quasi Deterministic Radio Channel Generator (QuaDRiGa) [24] project as an extension for the WINNER+ (the 3-D version of the WINNER II) [25] work. They presented a 3-D stochastic channel modeling approach introducing the time evolution [26; 27] of the channel, which is fully available as MATLAB code [28]. The QuaDRiGa model is divided into two steps; in the first one, they offer a stochastic generation of the large scale parameters (LSPs) and the random 3-D positioning of the scattering clusters. They consider base stations with a fixed position while the user terminals can move, so that the scattering clusters are fixed and the channel is deterministic in its time evolution. The second phase is the validation of the model by measurements in downtown Berlin (Germany) by extracting single-link and multi-link parameters and observing that there is a good match between simulated and measured results. In its last version, QuaDRiga can support carrier frequencies in the range 0.45-100 GHz. An example of geometry-based stochastic channel model (GBSM) is the mmMAGIC channel model [29], which is based on QuaDRiGa. Several measurements have been conducted in the main scenarios in the range between 6 GHz and 100 GHz.