# Reinforcement Learning Approach for Adaptive C-V2X Resource Management
## 4 Simulation Results  
The following section discusses the simulation results for the static MCS index as a benchmark compared to the proposed RL approach for the adaptive MCS index. As mentioned before, a higher MCS index will result in higher transport block capacity. However, the larger capacity means higher power consumption. On the other hand, using a lower MCS index does not mean the overall system performance will become the finest. A lower MCS index will result in a smaller transport block capacity. Although it may cause a lower power consumption, a small transport block may result in higher resource blocks allocation. The total number of resource blocks is bound to the corresponding channel  
Figure 4: PRR performance for static MCS index value against vehicle’s distance. bandwidth, as stated by the 3 GPP. Hence, if the number of resource blocks allocated to each vehicle is massive, then it will lead to a need for more radio resources. Figure 5 shows that the MCS index 11 achieved the best PRR performance among the other static MCS index values. This figure shows that the MCS index 11 might be sufficient for the corresponding environment. The transmission power amount was sufficient overall to provide the SINR threshold for the given MCS index 11. However, more was needed to satisfy the 90% PRR performance. The MCS index 11 PRR performance plunged below 90% at the vehicle's distance of 80 m and beyond. In order to satisfy the demand for reliability and a low latency trend, the acceptable PRR is generally above 90%. Figure 5 also shows that our proposed RL approach maintained a 90% PRR performance up to 130 m of the vehicle's distance. The total number of vehicles in the first scenario was set to 100 vehicles. Our proposed RL approach has proven capable of adaptively learning the vehicle's environmental situations and adjusting the corresponding MCS index. MCS index adjustment is carried out by comparing the actual PRR with the past steps PRR. Thus, the \"on-the-run\" methods may continuously learn. Our proposed method's overall PRR performance improvement was about 10% for the \(n_{V}=100\) vehicles scenario. The overall PRR performance comparison for the \(n_{V}=200\) vehicles scenario is shown in Figure 6. From Figure 6, it can be seen that the MCS index 11 achieved the best overall performance from the other static MCS index values. Nevertheless, the static MCS indexes were not performing well under the massive vehicle number. Even the MCS index 11 plunged under 90% PRR performance in just around 25 m of vehicle distance. Despite the overall performance in the \"high-density\" scenario still needing improvements, our proposed RL approach achieved much better than the static MCS index. The RL approach could satisfy the 90% PRR performance up to 60 m of the vehicle's distance under a high-density scenario. From both Figures 5 and 6, we can observe that the performance from the high MCS indexes, 20 and 27, were deficient. The case of MCS 20 and 27 may be caused by a lack of transmission power in order to satisfy the size of the large chunk of transport blocks carried by the corresponding MCS index. The large transport blocks size may have been of benefit in the large beacon size scenario. Focusing on the very beginning of Figure 4, the MCS index 20 achieved the highest PRR performance. However, the performance immediately plunged significantly as the vehicle's distance increased. For the \(n_{V}=200\) vehicles scenario, our proposed methods achieved an overall PRR performance improvement of 21%. Figure 5: Overall PRR performance comparison for \(n_{V}=100\) vehicles scenario. In order to investigate the proposed RL approach, Figures 7 and 8 show the PRR and rewards evolution against the LTE events number. Figure 7 shows the PRR results taken from the different learning rates, \(\alpha=0.1\) and \(\alpha=0.9\). The lower alpha means the system will prefer not to refrain from learning anything from the newly selected action (MCS index).