## 5 Simulation Results and Discussions  
In this section, the performance of the proposed algorithm was compared with that of other existing algorithms. Simulations were carried out using Tensorflow 2.0 and the Simulation of Urban Mobility (SUMO) simulator [23]. In the simulations, the multi-cell H-CRAN environment was considered; the parameters are summarized in Table 2. The parameters and system requirements are set in accordance with the 3 rd Generation Partnership Project (3 GPP) specifications release 15 and 16 [20, 24]. In addition, Rayleigh fading, log-normal shadowing, and the path-loss model(\(140.7+36.7\log(distance)\)) were considered [25]. The DQN adopted for the simulation was a fully connected neural network consisting of an input layer, a hidden layer, and an output layer. The number of neurons in the hidden layer was 256 and ReLu was utilized for the activation function. The DQN parameters had learning rate \(\alpha=0.01\), discount factor \(\beta=0.9\). The replay memory size is 3000, network update frequency is 2, and target network update frequency is 30. Each network update occurs every unit episodes.The latency and reliability requirements were 10 ms and an outage threshold of 3 dB, respectively. In the multi-cell environment, 43 RRHs were deployed at the center of intersections, and the vehicles were distributed and moved on the roads. The Luxembourg SUMO traffic Scenario [26] dataset was used for the mobility and intersection simulations to create a scenario that would meet common requirements and contain realistic traffic demand and mobility patterns. With this dataset, we composed two types of scenarios: Low traffic and high traffic. As the Figure 3, the high traffic scenario shows mobility during rush hour peaks around 20:00 with high traffic demands for one day. In the low traffic scenario, the required traffic demand is about half that of the high traffic scenario. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Notation** & **Value** \\ \hline Noise power spectral density & \(N\) & \(-174\,\mathrm{dBm}/\mathrm{Hz}\) \\ Total bandwidth & \(W^{total}\) & 100 MHz \\ SINR threshold & \(\gamma_{0}\) & 0.5 dBm \\ Maximum outage probability constraint & \(\tau_{max}\) & 0.05 \\ Circuit power of RRH & \(\tau_{5}\) & 4.3 W \\ Slope of RRH & \(\Delta slope\) & 4.0 \\ Circuit power of fronthaul transceiver and switch & \(\pi_{fronthaul}\) & 13 W \\ Power consumption per bit/s & \(\varphi\) & 0.83 W \\ Transmission power of V2I mode & \(p_{E}^{c}\) & 23 dBm \\ Transmission power of RRH & \(p_{tr}^{b}\) & 24 dBm \\ Cluster size of RRH grouping & \(N_{s}\) & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters used in the simulation. To evaluate the performance of the proposed algorithm, we used four metrics: Average SINR, average achievable data rate, system energy efficiency, and outage probability. The SINR is the important factor to show interference condition of vehicles, so the average SINR of total vehicles is used to show the increase or decrease in interference due to resource allocation. The average achievable data rate means system capacity that can be achieved in a given bandwidth, and it used to show efficiency of resource allocation method. The system energy efficiency means the achievable data rate per energy consumption, it is the main objective of our proposed algorithm. So the system energy efficiency is used to evaluate main performance of proposed algorithm. The outage probability is also used to show ensuring QoS constraint. We also simulated with four metrics as a function of the variation in V2V communication distance and maximum cluster size. Because V2V communication distance is a parameter related to cluster size and QoS of the vehicle, we compared the performance as a function of the change in V2V communication distance.