## 2 System and Channel Model  
### Scenario Overview  
In an electric vehicle charging communication network, there are two parts: uplink and downlink communication between the base station and vehicles and uplink and downlink communication between the base station and charging stations. User demands are input information in the network, and this algorithm aims to optimize the channel capacity between base stations and vehicles while maximizing energy efficiency. By dynamically adjusting communication resource allocation strategies to meet the changing needs of different users, the algorithm achieves more flexible and efficient resource utilization by monitoring and analyzing changes in user demands and considering the real-time status of network resources. Furthermore, the application of RIS technology makes the utilization of network resources more flexible and efficient by adjusting the direction and intensity of electromagnetic wave transmission, enhancing signal coverage and the transmission accuracy and thus improving the network resource utilization efficiency while meeting users' charging service needs. Additionally, the algorithm optimizes resource allocation strategies to avoid resource waste and overuse by integrating techniques such as causal reinforcement learning, maximizing network resource utilization while meeting user demands and thereby enhancing the overall network efficiency and performance. In summary, the A3C resource allocation control algorithm, assisted by an RIS, effectively balances user demands (electric vehicle charging) with network resource utilization, thereby improving the performance and efficiency of smart electric vehicle charging communication networks. This paper focuses on the downlink communication process between the base station and vehicles. In this network, using RIS-assisted communication can optimize the channel, enhance communication reliability, and improve performance. The communication process in electric vehicle charging involves uplink and downlink transmissions between vehicles and base stations, charging stations, and base stations with reflective intelligent surface (RIS) assistance. We introduce the communication between vehicles and the base station in the following. \begin{table}
\begin{tabular}{c c c} \hline Aspect & Existing Contribution & Advancements in \\  & & Provided Algorithms \\ \hline \multirow{2}{*}{Encoder} & \multirow{2}{*}{Simple encoder architectures} & Utilization of a deep encoder \\  & & with the self-attention \\  & & mechanism \\ \hline \multirow{2}{*}{Captures non-linearity} & Linear or shallow & Deep neural networks \\  & neural networks & capturing complex \\  & & relationships \\ \hline \multirow{2}{*}{Self-attention mechanism} & No or only basic & Integration of self-attention \\  & attention mechanisms & for capturing dependencies \\ \hline \multirow{2}{*}{Causal factor extraction} & Manual feature & Automatic extraction of causal \\  & engineering approaches & factors from state vectors \\ \hline \multirow{2}{*}{Resource allocation} & Fixed or rule-based & Dynamic allocation \\  & allocation methods & optimization using the \\  & & CF-A3C algorithm \\ \hline \multirow{2}{*}{Scalability} & Limited scalability for & Scalable solutions suitable for \\  & large systems & complex network scenarios \\ \hline \multirow{2}{*}{Real-world applicability} & Limited applicability in & Practical solutions with a \\  & practical settings & demonstrated \\ \multicolumn{2}{c}{real-world impact} \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison with existing contributions. Uplink Communication Process  
Vehicles Send Data to yir Base Station: Vehicles transmit uplink data to the base station through antennas based on their needs. These data may include the current status of the vehicle, charging demands, vehicle location, etc. Downlink Communication Process  
The Base Station Sends Data to Vehicles: The base station transmits downlink data to vehicles through antennas.