# Performance of Fuzzy Inference System for Adaptive Resource Allocation in C-V2X Networks
## 4 Simulation Results
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulation time & 60 s \\ Vehicle position update time & 0.1 s \\ Vehicle speed & 80 km/h \\ Vehicle speed standard deviation & 40 km/h \\ Road length & 2000 m \\ Number of lanes & 3 per directions \\ Total number of vehicles (\(n_{V}\)) & 200, 400 \\ Bandwidth & 10 MHz \\ Transmission power & 23 dBm \\ Beacon size & 190 Bytes \\ Modulation coding scheme (MCS) & 7 \\ Sensing interval & 0.1 s \\ Probability resource keep (\(P_{rk}\)) & 0, 0.2, 0.4, 0.6, 0.8 \\ SINR threshold & 8.47 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter settings. Figure 9: Overall PDR performance comparison for \(n_{V}=200\) vehicle scenario. From the figures, it can be seen that the results from the proposed FIS method achieved the best overall PDR performance. In Figure 9, the performance improvement is slight. This occurred because the number of radio resources was adequate to serve the total number of vehicles, which was 200. Figure 9 shows that even though the performance difference was small, the proposed FIS proved it can contribute more to better performance results. For the results of the more congested scenario, as shown in Figure 10, it can be seen that the proposed FIS method performance underperformed for shorter vehicle distances. This result was achieved because, at shorter distances, many disturbances occur from the congested situation. Hence, some vehicles also suffer from a poor Line of Sight, significantly reducing the overall packet delivery performance. However, beyond the 70 m distances, the proposed FIS method outperformed the fixed \(P_{rk}\) value and the matching table methods. It is possible that these results are limited to the performance results of a transmission power of 23 dBm. With the current transmission power, the vehicle communication coverage is affected. The increase in neighboring vehicle numbers also intensifies the competition for radio resources. The current simulation also used MCS 7 for all scenarios. Nevertheless, the role of \(P_{rk}\) in supporting the semipersistent foundation was well demonstrated. However, more research on this topic needs to be undertaken before the association between \(P_{rk}\), and PDR is more clearly understood. The proposed FIS method proved that it can achieve better Packet Delivery Ratio (PDR) performance than the fixed Resource Keep Probability (\(P_{rk}\)) value and the matching table (if-condition) method. It can be seen from Figures 9 and 10 that the proposed FIS method outperformed the other methods in both sparse and crowded vehicle scenarios. Because the nature of the FIS relies on the fuzzy rule design, there is still much room for further enhancement. We designed a matching table simulation to explore the optimal adaptive solution for determining the \(P_{rk}\) value. Nevertheless, the matching table performed poorly. Hence, we proposed the FIS method with two inputs, distance and CSI, to derive fuzzy output \(P_{rk}\). The simulation results showed that the proposed FIS method could outperform the other methods for either sparse or congested road conditions, with a total number of vehicles (\(n_{V}\)) of 200 and 400, respectively. Conceptualization, Y.-F.H. and J.-K.C. ; Investigation, T.I.B. and Y.-F.H. ; Methodology, T.I.B. and Y.-F.H. ; Software, T.I.B. and J.-K.C. ; Supervision, Y.-F.H. and J.-K.C. ; Writing--Original Draft, T.I.B. and Y.-F.H. ; Writing--Review and Editing, T.I.B. and Y.-F.H. All authors have read and agreed to the published version of the manuscript. This work was supported by the Ministry of Science and Technology of Taiwan under grant MOST-111-2221-E-324-018. Not applicable. The authors declare no conflict of interest.