# Attention-Guided Wireless Channel Modeling and Generating
## 2 Related Works  
In this study, clear inclusion and exclusion criteria were established to ensure that the selected literature is closely related to the latest advancements in wireless channel modeling. The inclusion criteria are as follows: first, only studies relevant to wireless channel modeling are considered, including traditional statistical models, physical models, and recent methods based on machine learning and deep learning; second, particular attention is given to the application of GAN in channel modeling and generation, especially studies published in recent years; finally, the included studies must provide a detailed description of the modeling methods, simulation processes, and experimental results, and demonstrate significant influence and citation count. The exclusion criteria include the following: first, studies unrelated to channel modeling, such as those focusing solely on communication protocols, network architectures, or other non-modeling approaches; second, early-stage studies or those not reflecting the current technological advancements,  
Figure 2: Frameworks of SE-GAN. particularly those that do not consider 5G/6G environments; and finally, literature that is not publicly available, such as preprints, not peer-reviewed, or commercial reports. Wireless channel modeling is a key link in the design and optimization of wireless communication systems, as well as a basic task for the theoretical research and practical implementation of modern wireless communication systems [7]. An accurate channel model helps to understand the specific physical effects of different wireless channels on transmitted radio signals, so as to design and deploy effective and feasible communication technologies for different transmission channels in practical application environments. However, the traditional channel modeling methods are mostly based on statistical and physical models. Although these traditional methods can accurately describe the characteristics of wireless channels in some cases, with the increasing complexity of wireless communication scenarios in the 5G and future 6G wireless communication systems [8], channel characteristics show strong time-variability and spatial heterogeneity, traditional wireless channel modeling methods are faced with many challenges. It is often difficult to adapt to the complexity and dynamics of the modern wireless communication environment and cannot provide sufficient accuracy and flexibility. In recent years, the rapid development of deep learning technology has provided new ideas and solutions for wireless channel modeling [9]. By using the powerful feature extraction and generation capabilities of deep learning models, the complex characteristics of channels can be automatically learned from channel data, and the complex characteristics of channels can be effectively captured [10] so as to achieve more accurate and flexible channel modeling and generation. One of the channel modeling methods is to use a precision channel detector to measure various channel parameters, including direction angle, arrival angle, delay, Doppler shift, etc. These key parameters are then combined to form the channel impulse response (CIR), which will play a key role in wireless network design and performance optimization. This channel modeling method using channel detectors for channel measurement is mostly based on time division multiplexing (TDM) [11], code division multiplexing (CDM) [12], and frequency division multiplexing (FDM) [13] to describe the characteristics of the channel. Typically, such channel measurement activities using channel detectors are time-consuming and limited to a few dedicated channel measurement environments. Storing the complete impulse response requires a lot of computational resources, especially in multipath channels, where the responses of multiple different paths need to be stored. Processing and analyzing measurement data results in high computational complexity.