# 5G V2X Performance Comparison for Different Channel Coding Schemes and Propagation Models
## 4 Simulation Results
### Simulation Results for 256-Bit Frame Length
At high SNR, all algorithms have zero BER, except SOVA, so in this case, our best choice can be modelled by other parameters, such as complexity, latency and other QoS parameters, depending on the nature of the communication and the services that must be provided. From Tables 11 and 12, we can make performance comparisons related to the traffic model and the channel state for the case of 512-bit frame. The results, as expected, are similar to the 128- and 256-bit frame scenarios. The impact of the channel state on the system performance is examined between scenarios 11 and 12 for the highway traffic model and among scenarios 13, 14 and 15 for the urban traffic model. Both groups of scenarios indicate that LOS channel state improves the decoding performance, but in a different way, which depends on the employed decoding algorithm and the traffic model. For example, LOS in LDPC has a great impact on the highway model, but this impact reduces in the urban model. Especially, at low SNR, there \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**BER** & **Scenario** & **BCJR** & **logBCJR** & **maxlogBCJR** & **SOVA** & **LDPC** & **Polar** \\ \hline \multirow{8}{*}{\(10^{-3}\)} & 11 & 2 & 2 & 2.3 & 3 & 4.2 & 3.7 \\ \cline{2-8}  & 12 & 2.7 & 2.7 & 3 & 3.9 & 4.3 & 4.1 \\ \cline{2-8}  & 13 & 2.4 & 2.4 & 2.7 & 3.5 & 4.7 & 4.4 \\ \cline{2-8}  & 14 & 2.7 & 2.7 & 3 & 3.9 & 4.2 & 3.9 \\ \cline{2-8}  & 15 & 2.7 & 2.7 & 3 & 3.9 & 4.3 & 4 \\ \hline \multirow{8}{*}{\(10^{-6}\)} & 11 & \(>\)3 & 3.7 & 3.8 & 7.4 & \(>\)6 & 5.8 \\ \cline{2-8}  & 12 & 4.6 & 4.7 & 4.8 & - & \(>\)7 & \(>\)7 \\ \cline{2-8}  & 13 & \(>\)4 & 3.9 & \(>\)4 & 8.5 & \(>\)6 & \(>\)6 \\ \cline{2-8}  & 14 & 4.4 & 4.5 & 4.8 & 10 & 6.8 & \(>\)7 \\ \cline{2-8}  & 15 & 4.6 & 4.5 & 4.8 & - & \(>\)7 & \(>\)7 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: SNR in dBs for BER values of \(10^{-3}\) and \(10^{-6}\), 512-bit frame. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**SNR (dB)** & **Scenario** & **BCJR** & **logBCJR** & **maxlogBCJR** & **SOVA** & **LDPC** & **Polar** \\ \hline \multirow{8}{*}{4} & 11 & 0 & 2.3 \(\times\)\(10^{-7}\) & 2.3 \(\times\)\(10^{-7}\) & 9.7 \(\times\)\(10^{-5}\) & 1.8 \(\times\)\(10^{-3}\) & 5.5 \(\times\)\(10^{-4}\) \\ \cline{2-8}  & 12 & 5.9 \(\times\)\(10^{-6}\) & 4.8 \  
is little difference between LOS, NLOS and NLOSv, mainly because LDPC fails to converge fast in this SNR region. The BER performance of BCJR-based algorithms has one order of magnitude difference between LOS and NLOSv-NLOS states. Highway-LOS scenarios seem to have a greater effect than urban-LOS scenarios. The first category is two orders of magnitude better than the second one, but the latter is only one order of magnitude better. From scenarios 11 and 13, we can deduce that LOS is affected by the different traffic models by an order of magnitude, on average, since the highway model is more reliable, having less obstacles than the urban model. Scenarios 14 and 15 show that the impact of the urban model is dominant in comparison to the effect that the NLOS and NLOSv channel states produce, since their differences are negligible. This is a fact also in scenarios 12 and 15, where we can examine the effect of the traffic environment on the NLOSv channel state. These scenarios have almost identical behaviour, but the highway scenario is slightly worse due to bigger small-scale variations that have their origin in Doppler effect. In summary, for scenarios 11 to 15, LDPC now has enough parity bits to exploit and become competitive in both the medium and high SNR ranges in order to achieve better BER performance than polar coding. Polar coding cannot increase its efficiency to the same extent as LDPC because the frame size remains too small to create good polarization conditions. BCJR-based algorithms further improve BER performance by taking advantage of the increased number of parity bits, which provide even more extrinsic information for the decoding process.