# 3 GPP 5G V2X Scenarios: Performance of QoS Parameters Using Turbo Codes
## 2 3 GPP 5G V2X QoS Scenarios and Proposed Dynamic System Model
In these scenarios the calculation of signal to interference and noise ratio (SINR) integrates the jamming/noise power into the denominator. C-V2X with the use of turbo codes is designed to facilitate decoding capability even at lower SNR values whereas for other wireless standards dedicated short range communication (DSRC) with convolutional codes requires higher SNR for successful decoding. Therefore, turbo codes represent a strong interference\(-\)tolerant channel coding scheme [22]. At our simulation scenarios of Table 2, we evaluate C-V2X direct communications towards 5G NR which also support Release 14 and Release 15 for security reasons [18, 19]. Specifically, the transmitter\(-\)receiver pair uses the 5G NR SL V2V unicast link and the transmitter vehicle shares information to the receiver for efficient maneuvers for cooperative driving. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Reliability** & **End-to-End** & **Data Rate per** & **Communication** \\
**Number** & **Description** & **(\%)** & **Latency** & **Vehicle (kbps)** & **Range** \\ \hline
1 & Cooperative awareness & 90–95 & 100 msto 1 s & 5–96 & Short to medium \\ \hline
2 & Cooperative sensing & \textgreater{}95 & 3 ms to 1 s & 5–25,000 & Short \\ \hline
3 & Cooperative maneuver & \textgreater{}99 & \textless{}3 to 100 ms & 10–5000 & Short to medium \\ \hline
4 & Vulnerable road user & 95 & 100 ms to 1 s & 5–10 & Short \\ \hline
5 & Traffic efficiency & \textless{}90 & \textgreater{}1 s & 10–2000 & Long \\ \hline
6 & Tele-operated driving & \textgreater{}99 & 5–20 ms & \textgreater{}25,000 & Long \\ \hline \hline \end{tabular}
\end{table} 
Table 1: 3 GPP 5G V2X scenarios. In addition, LTE\(-\)V2X is designed to enable the cooperative awareness service with the transmission of periodic messages by each vehicle to a road side unit (RSU) to inform about its status and movements [18,19]. Finally, at LTE\(-\)V2X the wireless transmission is combined with resource allocation, which is conducted by the RSU and is normally associated with a modulation and coding scheme (MCS) mechanism that selects parameters such as frame length, decoding algorithm, number of decoding iterations, etc. The proposed dynamic changing transmitter\(-\)receiver system, which uses 5G NR direct communication and is combined with an LTE network, can be seen at Figure 1. It also uses turbo channel coding scheme at physical layer. Based on Global Positioning System (GPS) and traffic\(-\)related information QoS performance indicators such as vehicle speed, data rate per vehicle, frame length and vehicle density (e.g., the specific value parameters from one of the 9 3 GPP scenarios presented in Table 2) the MCS mechanism will dynamically select the appropriate channel coding parameters. Subsequently, all the QoS parameters results such as throughput and end-to-end latency from the direct communication between transmitter\(-\)receiver will be used for a recursive feedback loop in a real\(-\)time QoS\(-\)based ML prediction model. This predictive model will also be installed in a real\(-\)time mobile edge computing (MEC) development in an urban scenario [1]. Thus, in this case, MCS will select dynamically not only the modulation specifications but also the channel coding conditions. Let's give more information on the simulated transmission of the transmitter\(-\)receiver pair, where the transmission power is 100 mW and results in a transmission range of less than 500 m. Furthermore, in our simulation model we transmit a total of 1,000,000 bits, which are divided into appropriate data frames. Subsequently, they are turbo encoded and send every TTI, to the mobile channel similarly to [20, 21, 22]. C\(-\)V2X in the direct mode operates in what is known as the intelligent transport systems (ITS) frequency band at 5.9 GHz. Finally, C programming language has been used to build the simulation model. For the simulated wireless channel model we do not use a simplistic deterministic path\(-\)loss model.