# An Adaptive Scheduling Mechanism Optimized for V2N Communications over Future Cellular Networks
## 5 Performance Evaluation  
In this section, we present the performance of the novel SOVANET scheduler against the OFDMA PF implementation of ns-3. The metrics used in assessing the proposed scheme's performance are mean throughput and mean delay, with the requirement goals summarized in Table 1.  
In Figure 3 the results regarding the mean values of throughput and delay for ToD UL are illustrated. The throughput gain for SOVANET vs. the PF scheduler for the 60 vehicles case reaches up to 21.8%, whereas for the 40 vehicles, it is 20%, and for the 25 vehicles case it is 4.4%. A massive increase in the delay of 1181% is observed for the PF scheduler in the 60 vehicles case, whereas it is 228% for the 40 vehicles, and 42% for the 25 vehicles have been recorded making a delay critical UC like ToD unfeasible when the PF scheduler is used. The above-mentioned performance gains are the result of assigning more resources for the critical cases and reducing the allocated resources for the non-critical cases. This way the vehicle is able to re-transmit lost packets without serious degradation in both the throughput and the experienced delay. At this point, it should be noted that the measured delay is considered end-to-end, taking into account an additional 12 ms user plane delay for packets going through the core network and up to a V2X server [7]. Evaluating the results regarding the ToD DL in Figure 4, both schedulers in the first three scenarios examined exhibit almost the same performance regarding the mean throughput and delay values. In the 60 vehicles case, the SOVANET scheduler demonstrates a 30.8% throughput gain and an 11.645% lower delay value. Once again, the overprovisioning of resources in the case of SOVANET leads to better performance, even in the case of DL when the network faces a significant load. Figure 3: ToD UL mean throughput and delay. Analyzing the performance of the HDM UL service, Figure 5 presents how SOVANET outperforms the PF scheduler. In the case of 60 vehicles, the throughput increases by 15.1%, whereas for 40 vehicles by 14.3%, and for 25 vehicles by 11.9% using the SOVANET scheduler. The mean delay in the case of the PF scheduler is 164.2% higher compared to SOVANET in the 60 vehicles case, 229% in the 40 vehicles case, 199.3% in the 25 vehicles case and 148.5% in the 10 vehicles case. Similarly, the UC of ToD using the SOVANET scheduler in all four scenarios, resulted in an overall higher efficiency of the proposed scheme against the PF scheduler because of the balanced allocation of resources for this critical service; the overprovisioning of resources ensures low delay including the cases for fast retransmission of data when this is needed. In the results presented in Figure 6 regarding the HDM DL, both schedulers' performance is similar for the first three cases. In the fourth and most demanding scenario, a considerable degradation of 63.2% in the throughput and a 1191% delay increase for the PF scheduler were recorded. Still, although SOVANET outperforms the PF scheduler, in the case of 60 vehicles, the SOVANET also illustrates a severe performance degradation in terms of delay. Figure 4: ToD DL mean throughput and delay. Figure 5: HDM UL mean throughput and delay. The analysis of the simulation results is completed with Figures 7 and 8, which present the throughput and delay performance of vehicles using the BSM UC. Regarding the UL, the performance of both schedulers was the same except for the case of 60 vehicles where PF shows a 10% throughput gain. The throughput in the DL of all cases was the same for both schedulers and in the case of 60 vehicles an increase in delay of 913% for the PF was computed. It should be noted that BSM is a non-critical case in terms of delay and low resource requirements; as a result, no remarkable allocation decision differences are applied by the two schemes and thus the performance for both schedulers is similar.