## 5 Simulation Results and Discussions
In Figure 6 it is observed that the outage probability is lower for the high scenario case than for the low scenario case. This is because in the high traffic scenario, the vehicles in the environment are more likely to be relatively close to the communication target because of the higher density of VE. The outage probability of the proposed algorithm is higher than that of _Compare1_ because there are more V2V mode VEs in the proposed algorithm than in _Compare1_, and there are more VEs that have the high variance of SINR. Within 150 m and 200 m, the proposed algorithm has lower outage probability than _Compare2_ because the V2V communication  
Figure 5: Comparison of achievable data rates with various V2V distance: (**a**) With low scenario; (**b**) with high scenario. rate is reduced as a result of high interference from the short V2V communication distance in high traffic scenarios. The system energy efficiency for different traffic scenarios are compared as a function of the V2V communication distance, as displayed in Figure 7. It is observed in Figure 7 that the system energy efficiency of all algorithms increases as the V2V communication distance increases. This is because when the V2V communication distance increases, the cluster size can increase to the extent that it is close to the limit. That is, as the number of V2V mode VEs increases, system energy consumption of the RRHs decreases and system energy efficiency increases. In the high traffic scenario, the performances of the proposed algorithm and that of _Compare1_ are particularly improved because the vehicle density is higher than in the case of the low scenario, allowing more VEs to communicate in the V2V mode, which could reduce system energy consumption. In addition, the proposed algorithm displays the highest energy efficiency by applying the clustering method to reduce system energy consumption and a DQN-based resource allocation to increase the achievable data rate. Figure 6: Comparison of outage probabilities with various V2V distances: (**a**) With low scenario; (**b**) with high scenario. In Figures 8-10, only the proposed and _Compare1_ algorithms are compared as a function of vehicle density change to evaluate the performance of the clustering method. The vehicle density change denotes a change in the average distance between vehicles when the total number of vehicles is fixed, which is different for the low and high scenarios in which the number of vehicles is changed. Unlike low and high scenarios, only the average distance between vehicles changes with interference based on the distance from the communication target. The average SINR and average achievable data rate for difference traffic scenarios are compared as a function of the cluster size, in Figures 8 and 9. The cluster size indicates size limit, and VEs can only be clustered up to the cluster size. In Figures 8 and 9, when the density is the same, the proposed algorithm displays the highest performance, and _Compare1_ displays the next highest performance. This is because the VEs that can receive the highest SINR in the proposed algorithm are selected as CH to configure the clusters. It can be also observed that performances in the case of high traffic scenarios are better than those of the low scenarios because the number of VEs in the high scenario increases as the distance between the vehicles is decreased. Interference also increases, but the performance  
Figure 7: Comparison of system energy efficiencies with various V2V distances: (**a**) With low scenario; (**b**) with high scenario. is still better because the benefits of getting closer to the communication target outweigh other considerations. The increase in density is also a result of the decrease in the average distance between vehicles, increasing the number of clustered vehicles. In each algorithm, the distance between VEs is on average 0.5, which is less than a density of 1.