# Attention-Guided Wireless Channel Modeling and Generating
## 2 Related Works
Deterministic channel modeling describes the input-output relationship of the channel according to the physical model and known parameters, and uses physical laws such as electromagnetic wave propagation, reflection, and refraction to describe the propagation process of signals in the channel [14]. The deterministic model assumes that the path of signal propagation is predictable and reproducible, and is not affected by random factors [15]. The shape, direction, and propagation characteristics of each path can be described by determined physical laws. Ray tracing model is a typical deterministic channel model. The ray-tracing model predicts the intensity, phase, and other characteristics of the signal at the receiving point by simulating the propagation path of electromagnetic waves (including reflection, diffraction, refraction, etc.) so as to predict the channel characteristics. Electromagnetic waves are traced as rays in a straight line [16]. This method assumes that the wave is smaller than the size of the obstacle encountered in the long run, allowing the wave effect to be ignored and only the geometric path of the ray to be considered. Ray-tracing models can provide accurate prediction of channel characteristics, but require detailed modeling of the environment, including geometric shapes and material properties of buildings, terrain, and obstacles [17]. If the environment model is not accurate, the results of the channel model may be distorted. In addition, the modeling process often requires a large amount of prior knowledge and data. Stochastic channel modeling regards channel characteristics as random variables and describes signal propagation characteristics in the channel through a probability distribution. This modeling method is based on stochastic processes and statistical principles, and takes into account the randomness and uncertainty of channel characteristics. The geometric stochastic channel model (GSCM) is a typical stochastic channel modeling method. GSCM is obtained by using the basic laws of the reflection, diffraction, and scattering of electromagnetic waves in the environment of many scatterers with a certain distribution [18], which can reproduce the random characteristics of different types of wireless channels with time, frequency, and space. GSCM can be described in terms of the selected parameters such as departure angle and arrival angle [19]. These parameters are randomly selected according to a specific distribution. However, in the real environment, the stochastic factors of the channel are usually complex and coupled [20]. In geometric-based stochastic channel models, although these factors can be modeled separately, their coupling relationships can be difficult to accurately capture, resulting in the limited fidelity and accuracy of the model. Channel modeling based on principal component analysis (PCA), using the PCA method to characterize and model multiple input multiple output (MIMO) channels. This method can extract some hidden features and structures from the measured channel data, and combined with the detailed information of the measurement environment and antenna configuration [21], effectively reconstruct the amplitude and phase of the CIR, which can effectively support the high antenna number of massive MIMO. PCA is a linear dimensionality reduction method, which assumes that the main information in the channel data can be extracted through linear combination [22]. However, in real wireless channels, many complex nonlinear factors may cause the channel characteristics to exhibit nonlinear behavior. PCA is unable to capture these nonlinear characteristics effectively, which may lead to a decrease in modeling accuracy. Digital twin technology creates a virtual replica of a physical system to dynamically reflect its behavior and state in real time.