## 4 Performance Evaluation and Discussion
### Experimental Results and Analysis  
The curves representing the CBR performance indicate that the proposed HPR-DCC consistently surpasses both the case without DCC and the original DCC. Nevertheless, the extent of improvement, when contrasted with the original DCC scheme, fluctuates depending on the specific congestion scenarios. Figure 4a demonstrates that the maximum convergence value of the proposed DCC is 30% when vehicle density is 40 veh/km, which does not significantly enhance the original DCC's improvement of 32%. Figure 5a, when vehicle density is 80 veh/km reveals that although the proposed HPR-DCC marginally outperforms the original DCC in congestion control, their maximum convergence values are nearly identical. In a highly congested setting with a vehicle density of 120 veh/km, as illustrated in Figure 6a, the proposed DCC attains a maximum CBR of 64%, while the original DCC scheme reaches a maximum CBR of 70%, exhibiting the most considerable gain of about 6%. In this research, we adopt a hybrid control approach that combines TPC and TRC mechanisms for congestion control and employs multiple active states to enhance channel load mitigation and vehicular communication. The proposed HPR-DCC leverages hybrid and distributed control design, offering enhanced control capabilities in high channel load scenarios. Through adaptive transmission facilitated by TPC, the transmit power  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Road length & 2 km \\ Lanes of the road & 3 lanes in each direction \\ Average vehicle density & 40, 80, 120 veh/km \\ Mean speed of vehicles & 140 km/h \\ Standard deviation of speed & 3 km/h \\ Message size & 1000 Bytes \\ Packet generation rate & 50 packets/s \\ Carrier sense threshold & \(-\)94 dBm \\ Data rate (default) & 6 Mbps \\ Transmission power range & 0\(-\)23 dBm \\ Packet transmission frequency range & 1\(-\)10 Hz \\ Target awareness ratio & 85\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: System-level simulation parameters.  
Since PRR serves as a critical indicator of packet transmission success rate, only PRR results exceeding 90% are considered to ensure a fair comparison. In comparison to the non-DCC scheme, the HPR-DCC scheme displays enhanced PRR performance across various test environments. In Figure 4b, in low congestion scenario when PRR is equal to 90%, the distance is extended by approximately 10 m relative to the original DCC scheme. A comparable outcome is depicted in Figure 5b, suggesting that the performance improvement of the HPR-DCC is not evident in low- and medium-congestion situations. In the high-congestion test scenario, the results presented in Figure 6b disclose that the effective reception distance of the proposed DCC is extended by 20 m compared to the original DCC, thereby significantly improving the system's signal reception performance. This enhancement arises from the incorporation of the distance between the control vehicle and neighboring vehicles into the state-switching strategy of the proposed HPR-DCC algorithm. When the gain of transmit power to the effective transmission distance surpasses a certain threshold, TRC is employed as an optimization supplement. Consequently, this improves channel conditions for vehicles located beyond the range of the original DCC.  
The results demonstrate that the proposed HPR-DCC scheme offers several advantages over the original schemes. Specifically, it performs better in highly congested environments, exhibiting an improved transmission range and a higher packet reception rate. Additionally, the proposed scheme maintains low channel occupancy.