# Emergency Communication System Based on Wireless LPWAN and SD-WAN Technologies: A Hybrid Approach
## 5 Scenario and Simulation Report
### Scenario 2: SD-WAN Simulation
Since there are no obstacles or objects between the transmitter and receiver causing path losses in our case, the BER was zero. In [51] the authors examine various PL mobile propagation LOS and non-LOS (NLOS) case studies, taking into account other effects such as the seasonal effect. They conclude that fixed wireless networks provide easy network connectivity in rural areas. The received data are reformed into an image, as seen in Figure 9b. In LTE networks, the physical layer baseband processing has strict real-time deadlines [56], so basebands signal spectrum analysis is an essential part of a simulation. Figure 9a depicts the Received Baseband LTE Signal Spectrum. \begin{table}
\begin{tabular}{l l} \hline \hline
**Decoding Procedure** & **Result** \\ \hline Generating LTE transmit waveform: & Setting channel map to ‘1’. \\ Packing image data into 30 frame(s). & \\ \hline SDR hardware sampling rate configured to & Starting a new RF capture. \\ capture 50 LTE RBs. & \\ \hline \multirow{3}{*}{Establishing connection to hardware.} & Corrected a frequency offset of \\  & 2.666334 \(\times\) 10\({}^{-1}\) Hz. \\ \cline{1-1}  & Detected a cell identity of 88. \\ \cline{1-1}  & Corrected a timing offset of 29,371 samples. \\ \hline Performing DL-SCH Decode for frame 1 of 30 & Successful MIB Decode. \\ in burst: & Frame number: 713. \\ \cline{1-1}  & Retrieving decoded transport block data. \\ \hline Performing DL-SCH Decode for frame 2 of 30 & Successful MIB Decode. \\ in burst: & Frame number: 714. \\ \cline{1-1}  & Retrieving decoded transport block data. \\ \hline \multirow{3}{*}{Performing DL-SCH Decode for frame 29 of 30 in burst:} & Successful MIB Decode. \\ \cline{1-1}  & Frame number: 711. \\ \cline{1-1}  & Retrieving decoded transport block data. \\ \hline Performing DL-SCH Decode for frame 30 of 30 & Successful MIB Decode. \\ in burst: & Frame number: 712. \\ \cline{1-1}  & Retrieving decoded transport block data. \\ \hline \multirow{3}{*}{Recombining received data blocks:} & EVM peak = 12.543\% \\ \cline{1-1}  & EVM RMS = 1.060\% \\ \cline{1-1}  & BER = 0.00000. \\ \cline{1-1}  & Number of bit errors = 0. \\ \cline{1-1}  & Number of transmitted bits = 8,084,832. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Decoding Procedure. Figure 8: (**a**) Equalized PDSCH Symbols; (**b**) Channel Magnitude Frequency Response. EVM is a well-liked system-level performance metric specified as a compliance test in various communication standards, including mobile communications (4G LTE, 5G). It is a valuable system-level indicator that quantifies the overall effect of all potential wireless system impairments through a single value [57]. Actually, it is the difference between the expected value of a symbol represented by a demodulated complex voltage and the value of the received symbol [58]. The results of the simulated scenario showed that the EVM \(\text{RMS}=1.060\%\) and the EVM peak \(=12.543\%\). Furthermore, 8084832 bits were transmitted without error (BER = 0.00000 (Number of bits in Error/Total number of bits transmitted)). The simulation tool showed that a one-base station SD-WAN architecture manages data transmission without errors in LOS conditions. Other (LOS and NLOS) BER simulation analysis for OFDM systems tested with various modulation methods has shown that the OFDM method gives almost a perfect performance, as the BER varies from 0.0002 to 0.2051 in the worst scenario [52]. Since the 1960s, researchers have studied the issue of simulating packet loss in networks. Given that packets might be lost due to many factors, including signal attenuation and refraction, multipathing, noise (including thermal noise), competition for media access, and other buffer problems, modeling packet loss in wireless networks is difficult. Numerous simulation models mentioned in [59] are compatible with wireless networks, including LPWAN and LTE. The authors of this study have demonstrated the utility of using PL models to simulate wireless system performance and to create novel strategies and algorithms.