# SDR-Based 28 GHz mmWave Channel Modeling of Railway Marshaling Yard
## 4 Results
### Impact of Typical Obstructions
#### 4.2.2 Impact of Adjacent Locomotive and Building  
As we mentioned in Section 2, the locomotive, whose length is 26.6 m, was parked on the adjacent track of \(T_{1}\) and covered the position from 52.4 m to 79 m.  
From Figure 16, we can see the number of MPCs of positions near this area slightly increase by one to two paths. In Figure 18, we can see the time-spread of positions near this area slightly increases; this is because, with the introduction of the locomotive, the reflection and scattering conditions of paths become more complex. We can quantify the impact on the time-spread with \(MAPE\):  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{Y_{i}-\hat{Y}_{i}}{Y_{i}}\right| \times 100\% \tag{10}\]  
We select data from 42.4 m to 102.4 m, which are near the locomotive parking area, for fitting in order to obtain a higher fitting accuracy, and the actual time-spread data in the green circle in Figure 22 are used as well as the fitting data to calculate \(MAPE\), as show in Figure 22.  
The linear fitting equation is:  
\[y=a+b\times x \tag{11}\]  
where \(a=84\) and \(b=2.5\). The actual data marked in the green circle are used to calculate \(MAPE\), and we obtain \(MAPE=38.63\%\).  
Now we analyze the impact of the building, which much larger than the locomotive, with a 7.5-m-high canopy and a 10-m-high main building, as shown in Figure 4. Since the locomotive and the building are basically set in parallel, we conduct the analysis through comparison of these two different objects, as shown in Figure 23.  
\begin{table}
\begin{tabular}{c c} \hline
**Position (m)** & **Block Object** \\ \hline
37.4 & Middle of the medal net \\
42.4 & Edge of the medal net \\
132.4 & Edge of the medal net \\
152.4 & Middle of the medal net \\
162.4 & Edge of the medal net \\
202.4 & Edge of the medal net \\
212.4 & Middle of the medal net (twice) \\ \hline \end{tabular}
\end{table}
Table 6: Blocking positions on \(T_{1}\).  
In Figure 23a, the difference of MPC numbers derived from samples collected on \(T_{1\&2}\) and \(T_{3}\) is not obvious, with a deviation of only 1-2 paths randomly, and a maximum number of 12 MPCs on \(T_{3}\).  
In Figure 23b, the lines represent the 95% statistical delay value of the first effective path and indicate that the delay parameters derived from samples collected on \(T_{1\&2}\) gradually increase as the distance increases, while the delay parameters derived from samples collected on \(T_{3}\) exhibit an irregular fluctuation state as the distances increases.  
In Figure 23c, the lines show the 95% statistical power values summed by position, and we see that the received power of \(T_{1\&2}\) gradually decrease as the distance increases, while the delay of \(T_{3}\) shows an irregular fluctuation state as the distances increases.  
In Figure 23d, the lines show the time-spread of the first and the last effective path, and we see that the time-spread of \(T_{1\&2}\) remains stable within a small range of 30 m, while the time-spread of \(T_{3}\) shows an irregular fluctuation state as the distances increases.  
Based on the above analysis, we can draw the conclusion that, due to the larger size and more irregular shape of the building, the received power, delay, and time-spread channel parameters within a small position range show dramatic random variations. Since normally there are several tracks in the railway marshaling yard, we recommend that locomotives with unloading data tasks travel on the tracks away from buildings, in order to obtain a more stable wireless transmission channel. In addition, considering the penetration loss caused by the Catenary, an additional signal-level protection margin needs to be added to achieve redundant protection and improve the reliability of the data transmission.  
Figure 22: Fitting of time-spread from 42.4 m to 102.4 m.