# Cooperative Transmission Mechanism Based on Revenue Learning for Vehicular Networks
## 5 Simulation Results and Comparative Analysis  
In the simulation parameter setting, a square area with 200 m side length is assumed as the space of the communication simulation [17] and a square space with 50 m side length is assumed as the hotspot area which is covered by a high-density small cellular network. The user positions are randomly distributed in this space to simulate the real situation. Meanwhile, we use the cost-231 Walfisch-Ikegami model [18] as the channel attenuation model. The channel attenuation model is \(L_{\text{los}}(\text{dB})\)\(=194.64+26 log\overbrace{\text{d}\text{d}\text{d}}^{\text{T}}(d)\) for the macro base station and \(L_{0}(\text{dB})\)\(=189.65+20 log(d)\) for the micro base station and vehicular network users. Other parameters are shown in Table 2.  
The communication rate of the vehicular network system is the sum of the rate of the macro base station and the rate of the micro base station, represented by \(\sum_{i=1}^{m}R_{i}=W^{macro}log_{2}\big{(}1+SINR_{i}^{macro}\big{)}+W^{pico} log_{2}\big{(}1+SINR_{i}^{pico}\big{)}\), where \(m\) represents the number of cell users. In the multi-point cooperative transmission mechanism, the communication rate of the system is the sum of the rate of the macro base station, the rate of the micro base station, and the cooperation rate of the vehicular network users, which is represented by \(\sum_{i=1}^{m}R_{i}=W^{macro}log_{2}\big{(}1+SINR_{i}^{macro}\big{)}+W^{pico} log_{2}\big{(}1+SINR_{i}^{pico}\big{)}+W^{users}log_{2}(1+SINR_{i}^{users})\). In Figure 5, the throughput of different network sizes is compared with different algorithm strategies. It can be seen from the figure that the space complexity increases with the increase in network scale. With the increase in the complexity of the network scale, the throughput of the joint anti-interference mechanism system gradually widens the gap with the throughput of the traditional dual-link mechanism, and the joint anti-jamming algorithm we adopted makes the total rate of the system rise faster. However, under the mechanism of the joint anti-jamming algorithm, there will always be vehicular network users temporarily stopping communicating with the base station. In order to make full use of these idle vehicular network users, we propose a multi-point cooperative transmission mechanism algorithm based on revenue learning. According to Figure 5, on the basis of the joint anti-interference algorithm, the multi-point cooperative transmission mechanism based on reinforcement learning algorithm is added. According to the overall throughput of the system, the system throughput of the multi-point cooperative transmission mechanism based on reinforcement learning algorithm is greater than that based on the joint anti-interference algorithm. \begin{table}
\begin{tabular}{c c} \hline \hline  
In the vehicular network system, the average throughput of a cell is represented by \(\left(\sum_{i=1}^{m}R_{i}=W^{macro}log_{2}\big{(}1+SINR_{i}^{macro}\big{)}+W^{ piclog}\big{(}1+SINR_{i}^{pico}\big{)}\right)\big{/}N\), where N represents the total number of cell base stations. In the multi-point cooperative transmission mechanism, the average throughput of a cell is represented by \(\left(\sum_{i=1}^{m}R_{i}=W^{macro}log_{2}\big{(}1+SINR_{i}^{macro}\big{)}+W^{ piclog}\big{(}1+SINR_{i}^{pico}\big{)}+W^{users}log_{2}\big{(}1+SINR_{i}^{ users}\big{)}\big{)}\right)/N\). In Figure 6, with the increase in the number of cell users, the average throughput rate of the cell gradually decreases, which is caused by the increasing interference of the interval. Our proposed joint anti-jamming algorithm mechanism can effectively suppress the interference between cells, and the throughput decreases more slowly than the traditional double-link technology. In addition,  
after several rounds of learning, the \(Q\) matrix can gradually converge to a better value so as to make a reasonable decision in multiple scheduling.