# Methodology for Simulating 5G and GNSS High-Accuracy Positioning
## 5 Performance Results
### Urban Scenarios with cmWave Deployments  
The urban scenarios are characterized by different building profiles with elevation masks above 30\({}^{\circ}\), as described in Section 2. However, the areas with high elevation masks, i.e., equal or above 50\({}^{\circ}\), only represent 1.4% of the total number of positions, while moderate elevation masks of 30\({}^{\circ}\) are observed in the 13.4% of the cases. Thus, the GNSS urban visibility is only reduced to around 8 GPS satellites in view, as shown in Figure 9a. In addition, there is also a slight degradation on the GDOP of the GNSS solution. This degradation introduces a reduction of 1% on the probability of GNSS position fixes. As it shown in Figure 11, the use of cmWave observables helps to achieve the full position availability in urban scenarios. However, as discussed in the previous section, the cmWave observables in NLoS conditions are still the dominant error of the hybrid position solution. In this sense, the exploitation of wideband cmWave signals for precise snapshot localization is limited by the harsh urban propagation conditions. Nonetheless, the knowledge of the NLoS conditions and the removal of NLoS observables improves the hybrid position accuracy, i.e., to 6 m on the 95% of cases. This improvement is obtained at the expense of a 0.5% of occasions without position fixes, due to the existence of only cmWave NLoS observables and lack of GNSS observables. Thus, advanced weighting techniques need to be used in future work to cope with this reduction on the hybrid positioning availability, in order to weight both cmWave NLoS and GNSS observables.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{2}{c}{**GNSS**} & \multicolumn{2}{c}{**Hybrid**} & \multicolumn{2}{c}{**Hybrid**} \\  & **(with Corrections)** & **(LoS/NLoS)** & **(Known LoS)** \\ \cline{2-7}  & \(\epsilon_{\text{3D}}\) & **GDOP\({}_{\text{3D}}\)** & \(\epsilon_{\text{3D}}\) & **GDOP\({}_{\text{3D}}\)** & \(\epsilon_{\text{3D}}\) & **GDOP\({}_{\text{3D}}\)** \\ \hline Urban, 5 BSs & 8.5 & 7.2 & 10.8 & 1.6 & 6.0 & 4.7 \\ Suburban, 4 BSs & 4.9 & 2.5 & 26.9 & 1.5 & 4.7 & 2.4 \\ Rural, 4 BSs & 4.8 & 1.9 & 5.5 & 1.4 & 3.3 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: 3D position error (in meters) and GDOP on the 95% of the cases for the GNSS positioning with differential or precise corrections and the hybrid GNSS and cmWave ToA positioning with and without LoS knowledge.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{2}{c}{\(\boldsymbol{M_{\text{BS}}=3}\)} & \multicolumn{2}{c}{\(\boldsymbol{M_{\text{BS}}=4}\)} & \multicolumn{2}{c}{\(\boldsymbol{M_{\text{BS}}=5}\)} \\ \cline{2-7}  & \(\boldsymbol{\epsilon_{\text{2D}}}\) & **GDOP\({}_{\text{2D}}\)** & \(\epsilon_{\text{2D}}\) & **GDOP\({}_{\text{2D}}\)** & \(\epsilon_{\text{2D}}\) & **GDOP\({}_{\text{2D}}\)** \\ \hline Urban & 100.8 & \(>\)10 & 60.5 & \(>\)10 & 20.4 & 4.4 \\ Suburban & 100.8 & \(>\)10 & 100.4 & \(>\)10 & 59.3 & 4.9 \\ Rural & 100.5 & \(>\)10 & 59.6 & \(>\)10 & 27.0 & 9.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: 2D position error (in meters) and GDOP on the 95% of the cases with stand-alone cmWave ToA positioning as function of the number of BSs.