# 5G V2X Performance Comparison for Different Channel Coding Schemes and Propagation Models
## 4 Simulation Results
### Simulation Results for 256-Bit Frame Length
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**BER** & **Scenario** & **BCJR** & **logBCJR** & **maxlogBCJR** & **SOVA** & **LDPC** & **Polar** \\ \hline \multirow{4}{*}{\(10^{-3}\)} & 6 & 2.7 & 2.7 & 2.9 & 3.8 & 4.6 & 4.0 \\ \cline{2-7}  & 7 & 3.5 & 3.5 & 3.7 & 4.9 & 5.8 & 5.4 \\ \cline{2-7}  & 8 & 3.2 & 3.2 & 3.5 & 4.4 & 5.3 & 4.7 \\ \cline{2-7}  & 9 & 3.5 & 3.5 & 3.8 & 4.9 & 5.7 & 5.3 \\ \cline{2-7}  & 10 & 3.4 & 3.4 & 3.6 & 4.9 & 5.8 & 5.4 \\ \hline \multirow{4}{*}{\(10^{-6}\)} & 6 & \textgreater{}5 & \textgreater{}6 & \textgreater{}6 & 8.4 & \textgreater{}7 & \textgreater{}6 \\ \cline{2-7}  & 7 & \textgreater{}6 & \textgreater{}6 & \textgreater{}7 & - & \textgreater{}8 & 7.6 \\ \cline{2-7}  & 8 & 6 & 5.8 & \textgreater{}6 & 10 & 7.5 & \textgreater{}7 \\ \cline{2-7}  & 9 & \textgreater{}6 & 6.4 & 6.7 & - & \textgreater{}8 & \textgreater{}8 \\ \cline{2-7}  & 10 & \textgreater{}6 & \textgreater{}6 & \textgreater{}7 & - & \textgreater{}8 & 7.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: SNR in dBs for BER values of \(10^{-3}\) and \(10^{-6}\), 256-bit frame. Scenarios 6 and 8 show the effect of the traffic model on the LOS channel state. The urban scenario needs a further 0.7 to 0.9 dB and less than 1 dB to achieve the 0.001 and 0.000001 BER objective, respectively. This means that the urban-LOS scenario needs approximately 1.25-times more power than the highway-LOS scenario to attain the same objectives. The impact of different channel states on the urban traffic model is examined for scenarios 8, 9 and 10. From Tables 9 and 10, we can conclude that an LOS channel is more affected by polar and BCJR-based algorithms at low SNR, in which BER is about 40-times bigger than the NLOS and NLOSv channel. At high SNR, the presence of the LOS channel has the same impact on all algorithms compared to the NLOS and NLOSv states. On the contrary, in the NLOS scenario, the urban traffic statistical features dominate and the channel statistical characteristics have a minor impact on system performance. The effects of different traffic models on NLOSv channel state are highlighted in scenarios 7 and 10. As in scenarios 2 and 5, we conclude that they have minor differences due to common small-scale characteristics. Scenarios 6 to 10 reveal the superiority of BCJR-based algorithms for 256-bit frames. BCJR-based algorithms further improved the BER performance because the convolutional coding process spreads information to all bits. As in the case of 128 bits, LDPC is suboptimal because the parity bits are not enough to provide the information needed to efficiently correct the data bits, especially in the low and medium SNR range, where noise significantly affects the received data. As the SNR increases, the inherent errors decrease and the ability of the LDPC to correct errors improves rapidly. Polar coding improved its performance, but not too much, because the frame size is too small to exploit the polarization. In the middle SNR region, polar has better performance, but LDPC has a higher rate of reduction and eventually becomes better at high SNR in scenarios 11-15. SOVA is the worst of all turbo algorithms but is better than LDPC and polar under 5.5 to 6.3 dB. Above this threshold, SOVA becomes the worst algorithm. Figure 4: BER vs. SNR for scenarios 11-15 and 512-bit frame length. (**a**) highway-LOS scenario; (**b**) highway-NLOS scenario; (**c**) urban-LOS scenario; (**d**) urban-NLOS scenario; (**e**) urban-NLOS scenario. Table 11 summarises the SNR requirements that we need to achieve 0.001 and 0.000001 BER for scenarios 11, 12, 13, 14 and 15 and for 512-bit frame length. From this table, it is more than obvious that BCJR-based algorithms are dominant and have about 2 dB difference from the 5G-NR decoding algorithms. Table 12 shows the performance for the decoding algorithms at 4 dB and at 9 dB SNR. From this table, we conclude that BCJR-based algorithms are the best choices for low-SNR cases.