# Attention-Guided Wireless Channel Modeling and Generating
###### Abstract  
Due to the fast advancement in wireless communication technology, the demand for the modeling and generating of wireless channels is increasing. Deep learning technology is gradually applied in the wireless communication field, and the Generative Adversarial Network (GAN) framework provides a new solution for channel modeling. This paper presents a method based on Wasserstein GAN with gradient penalty (WGAN-GP) guided by an attention mechanism for wireless channel modeling and generating. The feature extraction capability of the model is enhanced by adding a channel attention mechanism in WGAN-GP, and the representation capability of the model is enhanced by adaptively recalibrating the channel feature response. The experimental results demonstrate that the proposed approach accurately models the channel distribution and generates data that closely aligns with the real channel distribution. The proposed method has been shown to achieve superior qualitative and quantitative evaluation compared to the existing method. channel modeling; data generating; deep learning; WGAN-GP 2020 ac aca 2020 aca 202  
communication environments continuously evolve, the data required to update the model exponentially increases, further exacerbating resource burdens [4]. Deep learning technology has seen significant developments in recent years, and GAN provides a new approach to addressing these challenges. As shown in Figure 1, GAN can train a generative model by learning from a limited set of channel measurement data. This model learns the data distribution of the real channel dataset, obtains the channel distribution, and can generate a substantial volume of channel data consistent with the statistical characteristics of real channel environments [5]. Using GAN for channel modeling not only significantly reduces the amount of channel data required during the modeling process but also enhances the model's adaptability and flexibility. Wasserstein GAN (WGAN) effectively improves the mode collapse problem in traditional GAN training by using Wasserstein distance as a loss function. The WGAN with gradient penalty (WGAN-GP) further stabilizes the training process and enables the model to generate more realistic and diverse data samples. In the realm of this research, [PERSON] et al. [6] proposed a method named ChannelGAN based on GAN to generate a wireless channel model by learning channel measurement data. Although this study achieves significant results in the accuracy and efficiency of channel modeling, it still faces some limitations, particularly in the model's generalization capability and the diversity of the generated channel samples. ChannelGAN performs well on specific datasets, but its ability to generalize to different environments or never-before-seen channel conditions has not been fully validated. The generated channel samples may be limited in diversity, which may result in the inadequate adaptability of the model in the face of the complex and changeable environment of the real world. To address the above challenges, this paper introduces, for the first time, the integration of an attention mechanism with WGAN-GP in the domain of channel modeling and generating to improve the model's capacity for learning channel characteristics. We introduce an innovative method named SE-GAN for modeling wireless channel, as illustrated in Figure 2. Based on the WGAN-GP framework, we incorporate a channel attention mechanism. This attention mechanism explicitly models the dependencies between channels, thereby enhancing the model's capability to express and recalibrate channel features. This significantly improves the capability of the model to learn the characteristics of wireless channels and the realism of generated samples. By adaptively recalibrating the channel feature responses, the model's ability to capture important channel features is strengthened. This approach effectively enhances the accuracy and diversity of wireless channel samples without substantially increasing computational complexity.