## 7 5G Radio Propagation Mechanisms
### The 5G Channel Modelling
#### 7.5.3 Stochastic Model  
Stochastic (Statistical Model): Combining measurements and observations yields stochastic models. These models, which are based on substantial experimental data and statistical analysis, allow us to compute the received signal intensity in a particular propagation medium. A stochastic model takes into account all environmental impacts or effects on model development. Regardless, the model produces more accurate and effective predictions when utilized in contexts comparable to the one for which it was built. Consequently, the models are more accurate and suited for the region where the measurement campaign was carried out, and they will need to be modified for use in other places. Some stochastic models are discussed in more detail as follows. 1. Geometry-Based Stochastic Models (GBSM): GBSMs have been extensively employed for channel modeling in a variety of situations, which include performance, coverage, and rate analysis of 5G and beyond communication systems at both the FR1 and FR2 frequency bands [175; 176; 177]. The two types of GBSMs are those with a regular shape (RS) and those with an irregular shape (IS). It is assumed that scatterers in RS-GBSMs are stochastically dispersed in accordance with a particular geometry and a regular form. Effective scatterers are thought to be found in regular forms, such as ellipses, cylinders, one-ring, and two-rings. However, scatterers in IS-GBSMs are assumed to be stochastically distributed rather than situated in a regular shape. It manifests as effective scatterer locations with various shapes [123]. IS GBSMs include the WINNER II and 3 GPP spatial channel models (SCM). The channel modeling process starts with the network layout definition and determination of antenna parameters. Following that, the LOS/NLOS condition is assigned using the LOS probability model, and the PL is calculated using the PL model's small-scale parameters, such as delays cluster powers, and arrival and departure angles are then generated and randomly coupled. Finally, random initial phases are assigned, followed by the computation of channel coefficients. 2. Non-Geometrical Stochastic Models (NGSM): In V2V channel modeling, non-geometrical stochastic models, such as the tapped delay line (TDL) and clustered delay line (CDL) models, can also be employed. The channel impulse response (CIR) in TDL models is represented by a linear finite impulse response (FIR) filter. Each TDL model tap is made up of several MPCs with non-resolvable delays [178]. Using a correlation matrix, the TDL model provides a statistical description of the correlation between different antennas. In the 3 GPP part [179], three TDL models, known as as TDL-A, TDL-B, and TDL-C, were defined for representing three distinct channel profiles for the NLOS scenario, while TDL-D and TDL-E are for the LOS scenario. On the other hand, the CDL models are used for modeling signal direction in phase. It performs modeling on the basis of the description of directions of arrival and departure of the signal in space and the corresponding number of clusters to the number of channel reflections [178]. Just like for TDL, 3 GPP defined three CDL models, namely CDL-A, CDL-B, and CDL-C, for representing NLOS channel profiles, while CDL-D and CDL-E are for LOS channels. Another type of NCSM is the Correlation-Based Stochastic Model (CBSM), which tends to model the correlation between antenna elements to describe the spatial structure of a wireless channel (such as MIMO). 3. Saleh-Valenzuela (SV) Model: The SV-based model is commonly employed to simulate Channel Impulse Response (CIR) in indoor scenarios. In the SV model, it is assumed that rays reach clusters in the delay domain. The delay distribution follows a Poisson distribution, while the inter-arrival times are distributed exponentially. Cluster power decay rate, ray power decay rate, cluster arrival rate, and ray arrival rate are some of the parameters used to describe CIRs.