# Attention-Guided Wireless Channel Modeling and Generating
## 2 Related Works
The channel modeling method based on digital twin integrates sensor data, real-time monitoring, and simulation models to dynamically simulate channel characteristics in wireless communication environments, providing real-time adaptability to complex communication scenarios. In this method, a virtual model is first constructed to accurately reflect the physical properties of the communication system. The digital twin not only predicts based on historical data but also responds to environmental changes in real time, providing the system with accurate channel state information. The main advantage of digital twin technology lies in its ability to reflect changes in the physical world in real time [23]. This is particularly important for wireless communication systems, as channel conditions are often influenced by various factors, such as the environment, user behavior, and weather. Through digital twins, the communication system can dynamically adjust the channel model based on real-time data to improve communication quality and network performance. However, the real-time updating and simulation process of the digital twin model typically requires substantial computational resources. Channel modeling based on a machine learning algorithm uses machine learning technology to analyze and model wireless communication channels. Different from traditional channel modeling methods, machine learning algorithms model and predict channel characteristics by automatically learning patterns and features from a large amount of data [24]. It can capture complex nonlinear relationships and implicit features without relying on specific physical models or preset assumptions [25]. Clustering technology is a common machine learning method. Channel modeling based on clustering technology identifies channel state groups or patterns with similar propagation characteristics through cluster analysis of channel characteristic data, thus simplifying channel modeling methods. Clustering techniques aim to divide data into multiple categories or clusters based on their similarity [26]. In wireless channel modeling, clustering technology can help find regularity or structure in channel behavior so as to better understand and predict the performance of wireless channels [27]. Clustering technology groups channel characteristics by statistical methods, but usually does not directly consider the physical propagation mechanism of the channel. This can lead to a lack of physical interpretability in the channel model, making it difficult to provide a deep physical understanding [28]. The clustering method also needs a lot of data support to ensure the reliability and validity of the clustering results. If the amount of channel data are insufficient or the quality of the data are poor, the clustering results may be inaccurate, which will affect the effect of channel modeling. Channel modeling based on deep learning is a method that uses deep learning technology to model, predict, and optimize wireless communication channels. Deep learning builds and trains multi-layer neural network models to automatically extract features from data and make predictions [29]. Deep learning can recognize complex patterns and structures, and is widely used in speech recognition, image processing, natural language processing, and other fields. In recent years, it has been introduced into wireless communication [30], especially in channel modeling and prediction tasks. By learning the complex features of a large number of channel data, this method can more accurately simulate and predict the changes in signals in the propagation process [31], especially in a complex and dynamic wireless environment, which can improve the prediction accuracy of channels and network performance.