# Performance of Fuzzy Inference System for Adaptive Resource Allocation in C-V2X Networks
## 4 Simulation Results  
We simulated a 2 km highway scenario with three lanes in each direction. The vehicle speed was set to 80 km/h with a 40 km/h standard deviation. The first scenario consisted of 200 vehicles in total (\(n_{V}\)), and the second scenario will consist of 400 vehicles. For each \(n_{V}\), a simulation was run for the fixed \(P_{rk}\) scenario with \(P_{rk}=0,\ 0.2,\ 0.4,\ 0.6,\ \text{and}\ 0.8\); the matching table for the distance and CSI scenarios; and the proposed FIS methods. The PDR is the ratio of correctly delivered packets to the total number of transmitted packets. The PDR performance was plotted with the distance axes ranging from 0 to 150 m.  
\[PDR=\frac{Number\ of\ successfully\ delivered\ packets}{Total\ number\ of\ transmitted\ packets} \tag{3}\]  
The simulation was carried out based on the Monte Carlo method to investigate the effect of Resource Keep Probability on PDR performance. The random number seed we applied during the simulation was 1000, and each scenario was run 300 times. Figure 8 shows the simulation results for the fixed \(P_{rk}\) values of 0, 0.2, 0.4, 0.6, and 0.8. We conducted various simulation configurations with the corresponding fixed \(P_{rk}\) values. The most interesting finding was that none of the fixed \(P_{rk}\) value configurations could achieve 99% PDR performance for the given distance range. For the condition \(n_{V}\) = 200, the \(P_{rk}\) values 0.6 and 0.8 were observed to produce almost similar results, while the \(P_{rk}\) = 0 configuration produced the poorest performance. Similar situations occurred for the \(n_{V}\) = 400 condition, where the \(P_{rk}\) values 0.6 and 0.8 presented similar results, and the worst performance occurred when \(P_{rk}\) = 0. These are particularly useful findings, that the \(P_{rk}\) value has an important effect on PDR performance. Furthermore, a single fixed \(P_{rk}\) value is not always superior to another fixed value. As can be seen in both \(n_{V}\) conditions, \(P_{rk}\) 0.6 and 0.8 seemed to overcome one another at a certain distance. For instance, in the \(n_{V}\) = 200 condition, the highest performance shifting occurred at distances of 90 m and 120 m. For \(n_{V}\) = 400, the highest performance shifting occurred at a distance of 90 m. The present simulations raise the possibility that an adaptive \(P_{rk}\) value assignment can improve PDR performance. We used an LTEV2 Vsim [31] simulator based on MATLAB, developed and shared by University of Bologna, National Research Council (CNR), and National Inter-University Consortium for Telecommunications (CNIT) - Italy. The detailed parameter settings are  
Figure 8: PDR performance of fixed \(P_{rk}\) values for total vehicle numbers of \(n_{V}\) = 200 and 400.  
shown in Table 2. The simulation time (T) was 60, with a position update time resolution (\(t_{r}\)) of 0.1 s. The channel models used in the simulation were Line of Sight (LOS) and Non-Line of Sight (NLOS) for various V2V communications. This section presents evaluation of the comparison of the fixed \(P_{rk}\) value simulation, the matching table, and the proposed FIS methods. Figures 9 and 10 show the corresponding \(n_{V}=200\) and \(n_{V}=400\) vehicles scenarios, respectively. At a glance, it can be observed that the overall performance of the \(n_{V}=400\) simulations produced lower results than the \(n_{V}=200\) simulations. This occurred because of the difference in the number of vehicles. More vehicles in the system mean more interference in the wireless network; in addition, more vehicles scramble for the radio resources. From Figures 9 and 10, we can observe that even though the SB-SPS uses a probability approach (\(P_{rk}\)) to manage the radio resource allocation, the fixed \(P_{rk}\) value cannot achieve the best performance. From the figures, the colored dash lines represent the fixed \(P_{rk}\) values of 0, 0.2, 0.4, 0.6, and 0.8. The black colored lines represent the two conditions from the matching table scenarios. The solid red line is the result of the proposed FIS method.