## 2 Model Planning Procedures
### Dataset Origination and Parameters  
The dataset is taken from [14], where each scenario parameter is validated through the 3 GPP standard. These parameters describe the large-scale fading and small-scale fading parameters such as \(D_{\sigma}\), \(P_{L}\), \(K_{F}\), \(\sigma_{EoA}\), \(\sigma_{EoD}\), \(\sigma_{AoA}\), and \(\sigma_{AoD}\). The angular information is extracted from CIR using the space-alternating generalized expectation-maximization algorithm in MIMO model using 31 antenna elements [14; 15]. The CIR snapshots are generated from the reception of the signal from mobile terminal (MT) to base station (BS). These CIR snapshots are supposed to be processed from the BS end as shown in Figure 2.  
The NLoS and the LoS cases are obtained for both RMa and UMa, so the number of classes is four. As mentioned before, the UMa scenario is specified in urban places as it refers to a town or a city, while the RMa is defined for rural areas such as towns that have small population and reduced scattering.  
\(P_{L}\) is defined as a decrease of power due to a distance. It is the relation between the distance d and the actual path loss, denoted as \(P_{L}(d_{0})\), in a unique scenario and is expressed as \(P_{dB}\)[16]:  
\[P_{dB}=P_{L}(d_{0})+10\ \gamma\ log_{10}(\frac{d}{d_{0}})+S_{\sigma[dB]}, \tag{1}\]  
where the \(P_{L}\) exponent is defined as \(\gamma\), \(d_{0}\) is the reference distance, and \(S_{\sigma[dB]}\) represents the standard normal distribution.  
\(K_{F}\) is an essential small scale fading (SSF) parameter. It is the ratio between the power of a dominant LoS component and the NLoS multipath components. At every CIR snapshot capture, the \(K_{F}\) (\(K_{dB}\)) can be denoted as [17]  
\[K_{dB}=10\ log\{\frac{\left(\left|h(\tau_{m_{0}})\right|_{max}\right)^{2}}{ \sum_{\tau_{m}\
eq\tau_{m_{0}}}(\left|\ h(t)\right|)^{2}}\}, \tag{2}\]  
where \(\tau_{m}\) represents the current period of the \(m^{th}\) component delay, the highest amplitude occurs at \(\tau_{m_{0}}\) index, and \(m=1,2,3\ \dots\ M\). \(h(t)\) expresses the CIR in time domain. The value of \(K_{dB}\) is always greater in LoS scenarios.  
\(D_{\sigma}\) is also an important SSF parameter that indicates the channel dispersion of a CIR snapshot in terms of time delay. \(D_{\sigma}\) can be represented as [18]  
\[D_{\sigma}=\sqrt{\frac{\Sigma_{m=1}^{M}\left(\tau_{m}-\overline{\tau}\right) \left|\ h(\tau_{m})\right|^{2}}{\Sigma_{m=1}^{M}\left|\ h(\tau_{m})\right|^{2 }}}, \tag{3}\]  
where \(M\) denotes the total number of \(m^{th}\) components and \(\overline{\tau}\) is the mean excess delay and is denoted as  
\[\overline{\tau}=\frac{\Sigma_{m=1}^{M}\tau_{m}\mid h(\tau_{m})\mid^{2}}{\Sigma _{m=1}^{M}\mid h(\tau_{m})\mid^{2}} \tag{4}\]  
The channel capacity is dependant on \(D_{\sigma}\), where a scenario that has multiple rich scatters will have a larger \(D_{\sigma}\). Therefore, an NLoS scenario has a higher root mean square (RMS) delay spread (DS).  
Figure 2: LoS and NLoS scenarios.  
The angular spread (\(\sigma_{\theta}\)) denotes the channel dispersion for a CIR snapshot in terms of angular information, where \(\sigma_{\theta}\) is obtained as [19]  
\[\sigma_{\theta}=\sqrt{\frac{\Sigma_{m=1}^{M}\theta_{m,\mu}^{2}\cdot\mid h(\tau_{m })\mid^{2}}{\Sigma_{m=1}^{M}\mid h(\tau_{m})\mid^{2}}}, \tag{5}\]  
\[\theta_{m,\mu}=mod\big{(}\big{(}\theta_{m}-\overline{\theta}+\pi,2\pi\big{)} \big{)}-\pi, \tag{6}\]  
\[\overline{\theta}=\frac{\Sigma_{m=1}^{M}\theta_{m,\mu}\mid h(\tau_{m})\mid^{2 }}{\Sigma_{m=1}^{M}\mid h(\tau_{m})\mid^{2}}. \tag{7}\]  
The declaration of angle \(\theta\) is taken for azimuth angle of departure (AoD), azimuth angle of arrival (AoA), elevation angle of departure (EoD), and elevation angle of arrival (EoA). The NLoS scenario has more clusters than the LoS scenario. As a result, the value of \(\sigma_{\theta}\) is higher in NLoS scenarios than LoS scenarios.