# 3 GPP 5G V2X Scenarios: Performance of QoS Parameters Using Turbo Codes
## 3 QoS Analysis
### Throughput QoS Parameter  
In this section we investigate the effect of the throughput QoS parameter for the implemented 3 GPP scenarios of Table 2 for different SNR values. At Figure 9 the short frame length of 256 bits with a speed value 60 km/h is investigated (scenarios 1, 2). We observe that the lower data rate value (6 Mbps), the maximum capacity of the channel, a low SNR is required (over 4 dB) to achieve a throughput value over 2 Mbps for almost all decoding algorithms. On the other hand, the same performance can be achieved with a SNR value at about 6 dB using a higher data rate value per vehicle (12 Mbps) for almost all decoding algorithms, too.  
In Figure 10 the frame length of 256 bits with a speed value 100 km/h is investigated (scenarios 3, 4). We observe that with lower data rate values (scenario 3 with 6 Mbps data rate) a threshold SNR value of 4 dB is required to achieve a throughput value over 2 Mbps. On the other hand, the same performance can be achieved with a slightly smaller SNR value using a higher data rate value per vehicle (scenario 4 with 12 Mbps data rate), using SOVA algorithm. Using max-log-MAP, a very high SNR value is required in order to succeed the same throughput value at about 2 Mbps. Therefore, using max-log-MAP we can achieve a higher data rate closer to the channel capacity but with higher requirements  
Figure 9: Throughput vs. SNR for scenarios 1 and 2 of Table 2, considering different decoding algorithms, 3 iterations, frame length of 256 bits.  
in SNR. Finally, using SOVA we can improve the performance in throughput parameter at high SNR values.  
At Figure 11, we investigate simulation results with a frame length of 512 bits and a vehicle speed of 60 km/h (scenarios 5 and 6). Considering scenario 5, log-MAP and SOVA algorithms need a low SNR value (above 3 dB) for a throughput performance at about 2 Mbps, while max-log-MAP algorithm reaches the same levels of throughput efficiency with a higher SNR value (at about 5 dB). Subsequently, for scenario 6, log-MAP and max-log-MAP need a low SNR value (above 5 dB) for a throughput performance at about 2 Mbps, while SOVA reaches the same levels of throughput efficiency with a higher SNR value (at about 6 dB). Therefore, increasing the required data rate, we observe a significant performance reduction for SOVA.  
Figure 10: Throughput vs. SNR for scenarios 3 and 4 of Table 2, considering different decoding algorithms, 3 iterations, frame length of 256 bits.  
Finally, at Figure 12 simulation results with a frame length of 512 bits and a mobile terminal speed of 100 km/h are investigated (scenarios 7, 8). For scenario 7, we note that using SOVA we observe a non--zero throughput value with a lower SNR value at about 3 dB compared to the other two algorithms. On the other hand, considering scenario 8, only log-MAP can achieve the maximum channel capacity (12 Mbps) with the minimum SNR requirement (about 5 dB).  
Summarizing, it is obvious that by increasing the data rate without increasing the transmitting power, the communication range will be smaller. This is reasonable because near neighbors' vehicles get lower throughput values for short reaction time compared to more distant neighbors in cooperative communication. Therefore, the optimal location of the relay nodes that will replay the message sent by the transmitter must be investigated. Concluding, non--easily identifiable factors such as exact speed and location of vehicles, transmitting power, data rate, affect the selection of the optimal decoding algorithm in a dynamic adaptive scheme. Finally, packet losses due to the Doppler effect from increasing vehicle speed (from 60 km/h to 100 km/h) are not significantly reflected in performance due to the limitation of the channel capacity by the available data rate.  
Figure 11: Throughput vs. SNR for scenarios 5 and 6 of Table 2, considering different decoding algorithms, 3 iterations, frame length of 512 bits.