# Rainy Environment Identification Based on Channel State Information for Autonomous Vehicles
## 3 Wireless Communication Vehicle Network
It can provide ample feature representation path components, including its own gain, delay, and Doppler frequency shift parameters, which meticulously reflect the changes in signal propagation under rainy conditions. By adjusting these parameters, the five vehicular environments under rainy weather conditions can be accurately simulated based on channel modeling characteristics such as power attenuation, time delay spread, and Doppler frequency shift. During the training process, particular attention was given to vehicular environments under torrential rain conditions. Torrential rain is defined as a rainfall rate exceeding 50 mm/h. Such conditions significantly affect wireless signals, including notable signal attenuation and multipath effects. Due to the high mobility of vehicular environments, the transmitted messages are affected by the wireless channel. The signal received on the vehicular wireless channel can be represented as follows:  
\[Y(k)=X(k)\cdot H(k)+W(k) \tag{13}\]  
where \(X(k)\) represents the k-th transmitted data symbol, \(W(k)\) is the current noise, and \(H(k)\) represents the k-th wireless channel response, which is described by the CSI. Channel estimation at the receiver end is performed to calculate the CSI, which is crucial for recovering the transmitted data. In vehicular network communication, the Least Squares (LS) estimator is one of the most commonly used channel estimation methods, widely applied in the industrial implementation of V2X communication due to its low complexity. The mathematical expression for the LS estimator can be represented as follows:  
\[\hat{H}_{LS}=\min_{H_{LS}}\lVert Y-X-H\rVert_{2}^{2} \tag{14}\]  
where \(\lVert\,\cdot\,\rVert_{2}\) is the \(L_{2}\) norm, \(X\) is the long training sequence vector, and \(Y\) represents the corresponding observation vector. Due to multipath propagation and the Doppler effect, the received signal often becomes distorted, which is particularly problematic for OFDM-based systems. To ensure the reliability and efficiency of data transmission, it is necessary to introduce equalization strategies at the receiver end to counteract these distortions in OFDM-based systems. The proposed equalization strategy aims to optimize the received signal using channel estimation information, thereby effectively suppressing multipath and Doppler effects in both frequency and time domains. The adjustment based on channel estimation values is as follows:  
\[G_{0}=\left(\frac{\lambda}{4\pi d}\right)^{2} \tag{6}\]0  
where \(H_{eq}(k)\) is the k-th equalized channel response, \(H(k)\) is the k-th original channel response, \(N_{0}\) is the current noise power spectral density, and \(E_{s}\) is the current symbol energy. This equalization strategy optimizes the signal recovery process by considering the signal's energy and noise level, thereby improving the quality of the received signal and the overall performance of communication. At the receiver end of an OFDM system, the channel response is first obtained through the Least Squares method of channel estimation. Subsequently, Formula (11) is applied to adjust the signal of each subcarrier to counteract channel distortion and improve the reliability of the signal.