# Reinforcement Learning for Energy Optimization with 5G Communications in Vehicular Social Networks
## 5 Results and Discussion
As the number of devices capable of D2D communication increases as the D2D distance increases, the difference in system energy efficiency increases. This is because the devices communicate directly rather than through the RRH, which reduces the system's energy consumption. The proposed algorithm is designed to maximize system energy efficiency; thus, it gives the best performance in scenarios with large D2D distances. Furthermore, because D2D-mode vehicles communicate with the same transmission power at different distance thresholds, the achievable data rate and system energy efficiency decrease at longer D2D distance thresholds. This reveals that the overall performance decreases as the D2D distance threshold \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Notation** & **Value** \\ \hline Noise power spectral density & \(N\) & \(-174\) dBm\(/\)Hz \\ Total bandwidth & \(W^{total}\) & 100 MHz \\ SINR constraint & \(\gamma_{0}\) & 0.5 dBm \\ Maximum outage probability constraint & \(\tau_{max}\) & 0.05 \\ Minimum outage probability constraint & \(\tau_{min}\) & 0.01 \\ Circuit power of RRH & \(\mathcal{G}_{rh}\) & 4.3 W \\ Slope of RRH & \(\Delta\)_slope_ & 4.0 \\ Circuit power of fronthaul transceiver and switch & \(\phi_{fronthaul}\) & 13 W \\ Power consumption per bit/s & \(\ell\) & 0.83 W \\ Transmission power of cellular device & \(p_{tr}^{s}\) & 23 dBm \\ Transmission power of RRH & \(p_{tr}^{s}\) & 24 dBm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters used in the simulation. increases, but the proposed algorithm achieves a better performance compared to other algorithms. In addition, system energy efficiencies in the light traffic scenario get higher-scale results than the heavy traffic scenario. This is because vehicles require more resources in the light traffic scenario compared to the same number of vehicles in a heavy traffic scenario. Required resources affect system capacity, so system energy efficiencies in the light traffic scenario are higher than the heavy traffic scenario in the same system load situation. The average achievable data rates for the different D2D distance scenarios are compared as functions of system loads with different mobility scenarios, as shown in Figure 4. Figure 4a shows that the proposed algorithm has achievable data rates that are higher than those of BA, Compare1, and Compare2 by approximately 20%, 40%, and 22%, respectively. Figure 4b shows that the achievable data rate of the proposed algorithm is 60% higher than that of Compare1. Compared to BA and Compare2, it presents higher energy efficiency, with differences of approximately 16% and 19%, respectively. In Figure 4c, the proposed algorithm presents approximately 10%, 47%, and 13% higher achievable data rates compared to those of BA, Compare1, and Compare2, respectively. Figure 4d shows that the proposed algorithm outperforms BA, Compare1, and Compare2 by approximately 9%, 65%, and 11%, respectively. As the D2D distance increases, increasing the D2D communication ratio results in reduced energy consumption. However, increasing the D2D distance decreases the achievable data rates because the D2D mode communicates with the same transmission power even in degraded interference situations. In addition, communication without mode-selection algorithms, such as BA and Compare2, can select the transmission mode by simply considering the expected SINR, so it can perform better in terms of achievable data rate than algorithms with mode selection. Figure 3: Comparison of system energy efficiencies with various device-to-device (D2D) distances and system loads: (**a**) 250 m with light traffic; (**b**) 350 m with light traffic; (**c**) 250 m with heavy traffic; and (**d**) 350 m with heavy traffic. The proposed algorithm achieved a higher achievable data rate even in such scenarios by adjusting the target SINR.