# Cooperative Transmission Mechanism Based on Revenue Learning for Vehicular Networks
## 2 System Model and Tool Analysis  
### System Model  
In this paper, the system model of a vehicular network is built on the basis of a double link structure. As shown in Figure 1, there are macro base stations and micro base stations that provide services for vehicle-mounted users and transmit information at the same time. Macro base stations have wider communication coverage and can provide services for all users in the cell. However, its communication efficiency is usually low due to the influence of the system and communication frequency band. The problem of low communication efficiency can be solved by adding multiple micro base stations in the cell area covered by macro base stations. Therefore, the terminals have dual link capability, which can communicate with the macro and micro base stations at the same time for better service. In order to ensure the quality of communication, more micro base stations are usually deployed in the cell. However, the micro base stations will interfere with each other and the communication quality of end users is reduced. In fact, the interference of micro base stations, especially those at the edge of the cell, mostly comes from the base station of the adjacent cell. The end users in the edge region are far away from the macro base station of their own cell and receive a weaker signal, so they are more susceptible to the interference of the z signal sent by the base station of the adjacent cell. In order to solve the above problems and improve the communication service quality of the end users in the edge area, we need to obtain the signal strength and communication rate received by the end users. For end user \(i\), the signal to interference plus noise ratio (\(SINR\)) [4] received from macro base station \(g\) is expressed as follows:  
\[SINR_{i}^{macro}=\frac{P_{g}|h_{g}^{i}|^{2}}{\sigma^{2}} \tag{1}\]  
Figure 1: System model. For the terminal covered by the micro base station, the signal to noise plus interference ratio of the micro base station received by the terminal is shown as follows:  
\[SINR_{i}^{pico}=\frac{P_{e}|h_{e}^{i}|^{2}}{\sum_{j\
eq e}P_{j}|h_{e}^{j}|^{2}x_{ j}+\sigma^{2}} \tag{2}\]  
where \(Pg\) represents the transmit power of macro base station \(G\) located in the center of the cell, \(Pe\) represents the transmit power of micro base station \(E\), and \(h_{g}^{i}\) and \(h_{e}^{i}\) represent the path loss from the macro base station and the micro base station to terminal \(I\), respectively. \(\sigma^{2}\) represents the additive white Gaussian noise widely existing in the environment. For the dual-link network, due to the use of a large number of micro base station networks of the same system for network coverage, there is serious interference between the cells. \(\sum_{j\
eq e}P_{j}|h_{e}^{j}|^{2}x_{j}\) is the interference of the neighboring cells to the local cell, which is also the main objective of our optimization. After the signal to noise plus interference ratio is obtained, [PERSON]'s theorem is used to estimate the communication rate of the communication system in engineering. [PERSON]'s theorem [5] is as follows:  
\[R=Wlog_{2}(1+\frac{S}{N}) \tag{3}\]  
where \(R\) is the communication rate, \(W\) is the channel bandwidth, and \(S/N\) is the noise ratio. In the approximate estimation, we often use SINR instead of \(S/N\) to estimate the channel performance. Therefore, the limit communication rate of terminal \(I\) is estimated as follows. \[ \begin{split}& R_{i}=R^{macro}+R^{pico}\\ &=W^{macro}log_{2}(1+SINR_{i}^{macro})+W^{pico}log_{2}\Big{(}1+ SINR_{i}^{pico}\Big{)}\end{split}  \tag{4}\]  
The communication rate of terminal \(I\) consists of two parts. One part is the communication rate provided by the macro base station, and the other part is the communication rate provided by the micro base station. Based on the dual-connection technology, users can obtain services from different standard base stations at the same time, which greatly improves the quality of service of users and improves the utilization rate of base stations.